Computational intelligence techniques for medical diagnosis and prognosis: Problems and current developments

• Autorzy: Shahid Afzal Hussain , Singh M. P.

Identyfikatory

DOI

10.1016/j.bbe.2019.05.010

• Języki publikacji: EN

Abstrakty

EN

Diagnosis, being the first step in medical practice, is very crucial for clinical decision making. This paper investigates state-of-the-art computational intelligence (CI) techniques applied in the field of medical diagnosis and prognosis. The paper presents the performance of these techniques in diagnosing different diseases along with the detailed description of the data used. This paper includes basic as well as hybrid CI techniques that have been used in recent years so as to know the current trends in medical diagnosis domain. The paper presents the merits and demerits of different techniques in general as well as application specific context. This paper discusses some critical issues related to the medical diagnosis and prognosis such as uncertainties in the medical domain, problems in the medical data especially dealing with time-stamped (temporal) data, and knowledge acquisition. Moreover, this paper also discusses the features of good CI techniques in medical diagnosis. Overall, this review provides new insight for future research requirements in the medical diagnosis domain.

Słowa kluczowe

EN

computational intelligence; disease diagnosis; medical data

PL

inteligencja obliczeniowa; rozpoznanie choroby; dane medyczne

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2019
• Tom: Vol. 39, no. 3
• Strony: 638--672
• Opis fizyczny: Bibliogr. 388 poz., rys., tab.

Twórcy

autor

Shahid Afzal Hussain

• National Institute of Technology Patna, Patna 800005, India

autor

Singh M. P.

• National Institute of Technology Patna, Patna, India

Bibliografia

• [1] Jensen PB, Jensen LJ, Brunak S. Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet 2012;13(6):395. http://dx.doi.org/10.1038/nrg3208.
• [2] Greenspan H, Van Ginneken B, Summers RM. Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans Med Imaging 2016;35(5):1153–9. http://dx.doi.org/10.1109/TMI.2016.2553401.
• [3] Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L. Imagenet: a large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition. 2009. pp. 248–55. IEEE.
• [4] Weinstein JN, et al. The cancer genome atlas pan-cancer analysis project. Nat Genet 2013;45(10):1113.
• [5] de Montjoie J. Introducing the CDISC standards: new efficiencies for medical research. CDISC; 2009.
• [6] National Academies of Sciences and Medicine E. Improving diagnosis in health care. National Academies Press; 2016.
• [7] Cios KJ, Moore GW. Uniqueness of medical data mining. Artif Intell Med 2002;26(1–2):1–24.
• [8] Jain AK, Mao J, Mohiuddin K. Artificial neural networks: a tutorial. Computer 1996;3:31–44.
• [9] Topouzis F, Anastasopoulos E. Glaucoma – the importance of early detection and early treatment; 2007.
• [10] Smith RA, et al. Breast cancer in limited-resource countries: early detection and access to care. Breast J 2006;12:S16–26. http://dx.doi.org/10.1111/j.1075-122X.2006.0.x0020.
• [11] Sayburn A. Prostate cancer screening can save lives but it is too early for a national programme, study finds. BMJ 2014;349:g5055. http://dx.doi.org/10.1136/bmj.g5055'
• [12] Fulcher J, Jain LC. Computational intelligence: a compendium. Springer; 2008.
• [13] Fang R, Pouyanfar S, Yang Y, Chen S-C, Iyengar S. Computational health informatics in the big data age: a survey. ACM Comput Surv (CSUR) 2016;49(1):12.
• [14] Wang H, Xu Z, Pedrycz W. An overview on the roles of fuzzy set techniques in big data processing: trends, challenges and opportunities. Knowl Based Syst 2017;118:15–30. http://dx.doi.org/10.1016/j.knosys.2016.11.008.
• [15] Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput Biol Med 2018;100:270–8. http://dx.doi.org/10.1016/j.compbiomed.2017.09.017.
• [16] Blokh D, Stambler I. The application of information theory for the research of aging and aging-related diseases. Prog Neurobiol 2017;157:158–73. http://dx.doi.org/10.1016/j.pneurobio.2016.03.005.
• [17] Blokh D, Stambler I. The use of information theory for the evaluation of biomarkers of aging and physiological age. Mech Ageing Dev 2017;163:23–9. http://dx.doi.org/10.1016/j.mad.2017.01.003.
• [18] Fernandes M, et al. Systematic analysis of the gerontome reveals links between aging and age-related diseases. Hum Mol Genet 2016;25(21):4804–18. http://dx.doi.org/10.1093/hmg/ddw307.
• [19] Gilbert SF. Developmental Biology, 1. Palgrave Macmillan; 2000.
• [20] Avramopoulos D, Szymanski M, Wang R, Bassett S. Gene expression reveals overlap between normal aging and Alzheimer's disease genes. Neurobiol Aging 2011;32 (12):2319. e27–e34.
• [21] De Magalhães JP. How ageing processes influence cancer. Nat Rev Cancer 2013;13(5):357. http://dx.doi.org/10.1038/nrc3497.
• [22] Peysselon F, Ricard-Blum S. Understanding the biology of aging with interaction networks. Maturitas 2011;69(2):126–30.
• [23] Blokh D, Stambler I. Information theoretical analysis of aging as a risk factor for heart disease. Aging Dis 2015;6(3):196. http://dx.doi.org/10.14336/AD.2014.0623.
• [24] Na Y-J, Sohn K-A, Kim JH. Interpretation of personal genome sequencing data in terms of disease ranks based on mutual information. BMC Med Genomics 2015;8(2):S4.
• [25] Pawlak Z. International of Computer and Information Science. Rough set [J] 1982;11:341–56.
• [26] Zhang Q, Xie Q, Wang G. A survey on rough set theory and its applications. CAAI Trans Intell Technol 2016;1(4): 323–33. http://dx.doi.org/10.1016/j.trit.2016.11.001.
• [27] Pawlak Z. Rough sets and data analysis. Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium; 1996. p. 1–6.
• [28] Hassanien AE, Abraham A, Peters JF, Schaefer G. Rough sets in medical informatics applications. Applications of soft computing. Springer; 2009. p. 23–30.
• [29] Kryszkiewicz M. Rough set approach to incomplete information systems. Inf Sci 1998;112(1–4):39–49.
• [30] Tsumoto S. Mining diagnostic rules from clinical databases using rough sets and medical diagnostic model. Inf Sci 2004;162(2):65–80. http://dx.doi.org/10.1016/j.ins.2004.03.002.
• [31] Tripathy B, Acharjya D, Cynthya V. A framework for intelligent medical diagnosis using rough set with formal concept analysis; 2013 arXiv preprintarxiv:1301.6011.
• [32] Hamouda SKM, Wahed ME, Alez RHA, Riad K. Robust breast cancer prediction system based on rough set theory at National Cancer Institute of Egypt. Comput Methods Programs Biomed 2018;153:259–68. http://dx.doi.org/10.1016/j.cmpb.2017.10.016.
• [33] Dackus GM, et al. Long-term prognosis of young breast cancer patients (≤40 years) who did not receive adjuvant systemic treatment: protocol for the PARADIGM initiative cohort study. BMJ Open 2017;7(11):e017842. http://dx.doi.org/10.1136/bmjopen-2017-017842.
• [34] van der Hage JA, Mieog JSD, van de Velde CJ, Putter H, Bartelink H, van de Vijver MJ. Impact of established prognostic factors and molecular subtype in very young breast cancer patients: pooled analysis of four EORTC randomized controlled trials. Breast Cancer Res 2011; 13(3):R68. http://dx.doi.org/10.1186/bcr2908.
• [35] Inbarani HH. A novel neighborhood rough set based classification approach for medical diagnosis. Proc Comput Sci 2015;47:351–9. http://dx.doi.org/10.1016/j.procs.2015.03.216.
• [36] Zadeh LA. A note on Z-numbers. Inf Sci 2011;181(14):2923– 32. http://dx.doi.org/10.1016/j.ins.2011.02.022.
• [37] Wu D, Liu X, Xue F, Zheng H, Shou Y, Jiang W. A new medical diagnosis method based on Z-numbers. Appl Intell 2018;48(4):854–67.
• [38] Aliev R, Memmedova K. Application of Z-number based modeling in psychological research. Comput Intell Neurosci 2015;2015(11). http://dx.doi.org/10.1155/2015/037604.
• [39] Zadeh LA, Klir GJ, Yuan B. Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers. World Scientific; 1996.
• [40] Pourahmad S, Ayatollahi T, Mohammad S, Taheri SM. Fuzzy logistic regression: a new possibilistic model and its application in clinical vague status. Iranian J Fuzzy Syst 2011;8(1):1–17. http://dx.doi.org/10.22111/ijfs.2011.232.
• [41] Pourahmad S, Ayatollahi SMT, Taheri SM, Agahi ZH. Fuzzy logistic regression based on the least squares approach with application in clinical studies. Comput Math Appl 2011;62(9):3353–65.
• [42] Lee C-S, Wang M-H, Hagras H. A type-2 fuzzy ontology and its application to personal diabetic-diet recommendation. IEEE Trans Fuzzy Syst 2010;18(2):374–95. http://dx.doi.org/10.1109/TFUZZ.2010.4542042.
• [43] Torres A, Nieto JJ. Fuzzy logic in medicine and bioinformatics. BioMed Res Int 2006;2006. http://dx.doi.org/10.1155/JBB/2006/89190.
• [44] Gürsel G. Healthcare, uncertainty, and fuzzy logic. Digit Med 2016;2(3):101. http://dx.doi.org/10.4103/2226-8561.971946.
• [45] Mendel JM, John RB. Type-2 fuzzy sets made simple. IEEE Trans Fuzzy Syst 2002;10(2):117–27.
• [46] Liang Q, Mendel JM. Interval type-2 fuzzy logic systems: theory and design. IEEE Trans Fuzzy Syst 2000;8(5):535–50.
• [47] Leski JM, Henzel N. Time series of fuzzy sets in classification of electrocardiographic signals. Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013; 2013. p. 541–50.
• [48] Song Q, Chissom BS. Fuzzy time series and its models. Fuzzy Sets Syst 1993;54(3):269–77.
• [49] Malmir B, Amini M, Chang SI. A medical decision support system for disease diagnosis under uncertainty. Expert Syst Appl 2017;88:95–108. http://dx.doi.org/10.1016/j.eswa.2017.06.031.
• [50] Nazari S, Fallah M, Kazemipoor H, Salehipour A. A fuzzy inference-fuzzy analytic hierarchy process-based clinical decision support system for diagnosis of heart diseases. Expert Syst Appl 2018;95:261–71. http://dx.doi.org/10.1016/j.eswa.2017.11.001.
• [51] Mansourypoor F, Asadi S. Development of a reinforcement learning-based evolutionary fuzzy rule-based system for diabetes diagnosis. Comput Biol Med 2017;91:337–52. http://dx.doi.org/10.1016/j.compbiomed.2017.10.024.
• [52] Erkaymaz O, Ozer M, Perc M. Performance of small-world feedforward neural networks for the diagnosis of diabetes. Appl Math Comput 2017;311:22–8.
• [53] Gürbüz E, Kiliç E. A new adaptive support vector machine for diagnosis of diseases. Expert Syst 2014;31(5):389–97.
• [54] Mehmanpazir F, Asadi S. Development of an evolutionary fuzzy expert system for estimating future behavior of stock price. J Ind Eng Int 2017;13(1):29–46.
• [55] Soltani P, Shahrabi J, Asadi S, Hadavandi E, Johari MS. A study on siro, solo, compact, and conventional ring- spun yarns. Part III: Modeling fiber migration using modular adaptive neuro-fuzzy inference system. J Text Inst 2013;104(7):755–65.
• [56] Bajestani NS, Kamyad AV, Esfahani EN, Zare A. Prediction of retinopathy in diabetic patients using type-2 fuzzy regression model. Eur J Oper Res 2018;264(3):859–69. http://dx.doi.org/10.1016/j.ejor.2017.07.046.
• [57] Goudarzi M, Maghooli K. Extraction of fuzzy rules at different concept levels related to image features of mammography for diagnosis of breast cancer. Biocybern Biomed Eng 2018;38(4):1004–14. http://dx.doi.org/10.1016/j.bbe.2018.09.002.
• [58] Espinosa J, Vandewalle J, Wertz V. Constructing fuzzy models from input-output data. Fuzzy logic, identification and predictive control. 2005;21–58.
• [59] Domingos P, Pazzani M. On the optimality of the simple Bayesian classifier under zero-one loss. Mach Learn 1997;29(2-3):103–30.
• [60] Zhang H. Exploring conditions for the optimality of naive Bayes. Int J Pattern Recognit Artif Intell 2005;19(02):183–98.
• [61] John GH, Langley P. Estimating continuous distributions in Bayesian classifiers. Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence; 1995. p. 338–45.
• [62] Rennie JD, Shih L, Teevan J, Karger DR. Tackling the poor assumptions of naive bayes text classifiers. Proceedings of the 20th International Conference on Machine Learning (ICML-03) 2003;616–23.
• [63] Hasan MR, Hassan N, Khan R, Kim Y-T, Iqbal SM. Classification of cancer cells using computational analysis of dynamic morphology. Comput Methods Programs Biomed 2018;156:105–12. http://dx.doi.org/10.1016/j.cmpb.2017.12.003.
• [64] Karabatak M. A new classifier for breast cancer detection based on Naïve Bayesian. Measurement 2015;72:32–6. http://dx.doi.org/10.1016/j.measurement.2015.04.028.
• [65] Pena-Reyes CA, Sipper M. A fuzzy-genetic approach to breast cancer diagnosis. Artif Intell Med 1999;17(2):131–55.
• [66] Goodman DE, Boggess L, Watkins A. Artificial immune system classification of multiple-class problems. Proc Artif Neural Netw Eng ANNIE 2002;2:179–83.
• [67] Korzynska A, et al. The METINUS Plus method for nuclei quantification in tissue microarrays of breast cancer and axillary node tissue section. Biomed Signal Process Control 2017;32:1–9. http://dx.doi.org/10.1016/j.bspc.2016.09.022.
• [68] Alirezazadeh P, Hejrati B, Monsef-Esfahani A, Fathi A. Representation learning-based unsupervised domain adaptation for classification of breast cancer histopathology images. Biocybern Biomed Eng 2018;38(3):671–83. http://dx.doi.org/10.1016/j.bbe.2018.04.008.
• [69] Koller D, Friedman N, Bach F. Probabilistic graphical models: principles and techniques. MIT Press; 2009.
• [70] Larrañaga P, Moral S. Probabilistic graphical models in artificial intelligence. Appl Soft Comput 2011;11(2):1511–28. http://dx.doi.org/10.1016/j.asoc.2008.01.003.
• [71] Cooper GF, Herskovits E. A Bayesian method for the induction of probabilistic networks from data. Mach Learn 1992;9(4):309–47.
• [72] Heckerman D, Geiger D, Chickering DM. Learning Bayesian networks: the combination of knowledge and statistical data. Mach Learn 1995;20(3):197–243.
• [73] Liang F, Zhang J. Learning Bayesian networks for discrete data. Comput Stat Data Anal 2009;53(4):865–76.
• [74] Butz CJ, Hua S, Chen J, Yao H. A simple graphical approach for understanding probabilistic inference in Bayesian networks. Inf Sci 2009;179(6):699–716.
• [75] Fuster-Parra P, García-Mas A, Ponseti F, Palou P, Cruz J. A Bayesian network to discover relationships between negative features in sport: a case study of teen players. Qual Quan 2014;48(3):1473–91.
• [76] Fuster-Parra P, García-Mas A, Ponseti F, Leo F. Team performance and collective efficacy in the dynamic psychology of competitive team: a Bayesian network analysis. Hum Mov Sci 2015;40:98–118.
• [77] Charitos T, Van Der Gaag LC, Visscher S, Schurink KA, Lucas PJ. A dynamic Bayesian network for diagnosing ventilator-associated pneumonia in ICU patients. Expert Syst Appl 2009;36(2):1249–58.
• [78] Sesen MB, Nicholson AE, Banares-Alcantara R, Kadir T, Brady M. Bayesian networks for clinical decision support in lung cancer care. PloS One 2013;8(12):e82349. http://dx.doi.org/10.1371/journal.pone.0082349.
• [79] Djebbari A, Quackenbush J. Seeded Bayesian Networks: constructing genetic networks from microarray data. BMC Syst Biol 2008;2(1):57.
• [80] Needham CJ, Bradford JR, Bulpitt AJ, Westhead DR. A primer on learning in Bayesian networks for computational biology. PLoS Comput Biology 2007;3(8):e129.
• [81] Jansen R, et al. A Bayesian networks approach for predicting protein–protein interactions from genomic data. Science 2003;302(5644):449–53.
• [82] Lappenschaar M, Hommersom A, Lucas PJ, Lagro J, Visscher S. Multilevel Bayesian networks for the analysis of hierarchical health care data. Artif Intell Med 2013;57 (3):171–83. http://dx.doi.org/10.1016/j.artmed.2012.12.007.
• [83] Lycett S, Ward M, Lewis F, Poon A, Pond SK, Brown AL. Detection of mammalian virulence determinants in highly pathogenic avian influenza H5N1 viruses: multivariate analysis of published data. J Virol 2009;83(19):9901–10.
• [84] Poon AF, Lewis FI, Pond SLK, Frost SD. Evolutionary interactions between N-linked glycosylation sites in the HIV-1 envelope. PLoS Comput Biology 2007;3(1):e11.
• [85] Dutta D, Modak S, Kumar A, Roychowdhury J, Mandal S. Bayesian network aided grasp and grip efficiency estimation using a smart data glove for post-stroke diagnosis. Biocybern Biomed Eng 2017;37(1):44–58. http://dx.doi.org/10.1016/j.bbe.2016.09.005.
• [86] Liu S, et al. Quantitative analysis of breast cancer diagnosis using a probabilistic modelling approach. Comput Biol Med 2018;92:168–75. http://dx.doi.org/10.1016/j.compbiomed.2017.11.014.
• [87] Fuster-Parra P, Tauler P, Bennasar-Veny M, Ligeza A, Lopez-Gonzalez A, Aguilo A. Bayesian network modeling: a case study of an epidemiologic system analysis of cardiovascular risk. Comput Methods Programs Biomed 2016;126:128–42. http://dx.doi.org/10.1016/j.cmpb.2015.12.010.
• [88] Breiman L, Friedman J, Olshen R, Stone C. Classification and regression trees (cart) wadsworth. CA: Pacific Grove; 1984.
• [89] Aguiar FS, Almeida LL, Ruffino-Netto A, Kritski AL, Mello FC, Werneck GL. Classification and regression tree (CART) model to predict pulmonary tuberculosis in hospitalized patients. BMC Pulm Med 2012;12(1):40.
• [90] Arostegui I, et al. Combining statistical techniques to predict postsurgical risk of 1-year mortality for patients with colon cancer. Clin Epidemiol 2018;10(235). http://dx.doi.org/10.2147/CLEP.S291467.
• [91] Phakhounthong K, et al. Predicting the severity of dengue fever in children on admission based on clinical features and laboratory indicators: application of classification tree analysis. BMC Pediatr 2018;18(1):109. http://dx.doi.org/10.1186/s12887-018-y1078.
• [92] Shi KQ, et al. Classification and regression tree analysis of acute-on-chronic hepatitis B liver failure: seeing the forest for the trees. J Viral Hepat 2017;24(2):132–40. http://dx.doi.org/10.1111/jvh.71261.
• [93] Polikar R. Ensemble learning. Ensemble machine learning. Springer; 2012. p. 1–34.
• [94] Schapire RE. The strength of weak learnability. Mach Learn 1990;5(2):197–227.
• [95] Breiman L. Bagging predictors. Mach Learn 1996;24(2): 123–40.
• [96] Barandiaran I. The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 1998;20(8).
• [97] Ahn H, Moon H, Fazzari MJ, Lim N, Chen JJ, Kodell RL. Classification by ensembles from random partitions of high-dimensional data. Comput Stat Data Anal 2007;51 (12):6166–79.
• [98] Kumar I, Bhadauria H, Virmani J, Thakur S. A classification framework for prediction of breast density using an ensemble of neural network classifiers. Biocybern Biomed Eng 2017;37(1):217–28. http://dx.doi.org/10.1016/j.bbe.2017.01.001.
• [99] Colin C, Prince V, Valette P-J. Can mammographic assessments lead to consider density as a risk factor for breast cancer? Eur J Radiol 2013;82(3):404–11. http://dx.doi.org/10.1016/j.ejrad.2010.01.001.
• [100] Eng A, et al. Digital mammographic density and breast cancer risk: a case–control study of six alternative density assessment methods. Breast Cancer Res 2014;16(5):439. http://dx.doi.org/10.1186/s13058-014-0439-1.
• [101] Ekpo EU, Ujong UP, Mello-Thoms C, McEntee MF. Assessment of interradiologist agreement regarding mammographic breast density classification using the fifth edition of the BI-RADS atlas. Am J Roentgenol 2016;206(5):1119–23. http://dx.doi.org/10.2214/AJR.15.15049.
• [102] Kruk M, Kurek J, Osowski S, Koktysz R, Swiderski B, Markiewicz T. Ensemble of classifiers and wavelet transformation for improved recognition of Fuhrman grading in clear-cell renal carcinoma. Biocybern Biomed Eng 2017;37(3):357–64. http://dx.doi.org/10.1016/j.bbe.2017.04.005.
• [103] Kruk M, et al. Computer approach to recognition of Fuhrman grade of cells in clear-cell renal cell carcinoma. Anal Quant Cytopathol Histopathol 2014;36 (3):147–60.
• [104] Kruk M, Kurek J, Osowski S, Koktysz R. Improved computer recognition of Fuhrman grading system in analysis of clear-cell renal carcinoma. Proc. VIPIMAGE Conf., Canary Islands. 2015. pp. 221–6.
• [105] Freund Y, Schapire RE. Experiments with a new boosting algorithm ICML, vol. 96. 1996;p. 148–56. Citeseer.
• [106] Rodríguez JJ, Maudes J. Boosting recombined weak classifiers. Pattern Recognit Lett 2008;29(8):1049–59.
• [107] Cao J, Kwong S, Wang R. A noise-detection based AdaBoost algorithm for mislabeled data. Pattern Recognit 2012;45 (12):4451–65.
• [108] Wi H, Eibe F, Mining D. Practical machine learning tools and techniques. United State: Morgan Kauffman; 2011.
• [109] Ji C, Ma S. Combinations of weak classifiers. Adv Neural Inf Process Syst 1997;494–500.
• [110] Reyzin L, Schapire RE. How boosting the margin can also boost classifier complexity. Proceedings of the 23rd International Conference on Machine Learning; 2006. p. 753–60.
• [111] Friedman J, Hastie T, Tibshirani R. Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann Stat 2000;28(2):337–407.
• [112] Gutiérrez-Tobal GC, Álvarez D, del Campo F, Hornero R. Utility of adaboost to detect sleep apnea–hypopnea syndrome from single-channel airflow. IEEE Trans Biomed Eng 2016;63(3):636–46. http://dx.doi.org/10.1109/TBME.2015.2467188.
• [113] Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 2002;16:321–57.
• [114] Przystup P, Bujnowski A, Polinski A, Ruminski J, Wtorek J. Sleep apnea detection by means of analyzing electrocardiographic signal. Human–computer systems interaction: backgrounds and applications, 3. Springer; 2014. p. 179–92.
• [115] Breiman L. Random forests. Mach Learn 2001;45(1):5–32.
• [116] Friedman J, Hastie T, Tibshirani R. The elements of statistical learning (no. 10). Springer series in statistics New York; 2001.
• [117] Khalilia M, Chakraborty S, Popescu M. Predicting disease risks from highly imbalanced data using random forest. BMC Med Inform Decis Mak 2011;11(1):51.
• [118] Del Río S, López V, Benítez JM, Herrera F. On the use of MapReduce for imbalanced big data using random forest. Inf Sci 2014;285:112–37. http://dx.doi.org/10.1016/j.ins.2014.03.043.
• [119] Abraham B, Nair MS. Computer-aided diagnosis of clinically significant prostate cancer from MRI images using sparse autoencoder and random forest classifier. Biocybern Biomed Eng 2018;38(3):733–44. http://dx.doi.org/10.1016/j.bbe.2018.06.009.
• [120] Frank E, et al. Weka-a machine learning workbench for data mining. Data mining and knowledge discovery handbook. Springer; 2009. p. 1269–77.
• [121] He H, Bai Y, Garcia EA, Li S. ADASYN: adaptive synthetic sampling approach for imbalanced learning. 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence); 2008. pp. 1322–8.
• [122] Ng A. Sparse autoencoder. CS294A lecture notes, vol. 72. 2011;p. 1–19.
• [123] Mehrtash A, et al. Classification of clinical significance of MRI prostate findings using 3D convolutional neural networks. Proc SPIE Int Soc Opt Eng 2017;10134(Feb):11.
• [124] Armato SG, Petrick NA, Kitchen A, Seah J. Support vector machines for prostate lesion classification. Presented at the Medical Imaging 2017: Computer-Aided Diagnosis; 2017.
• [125] Armato SG, Petrick NA, Liu S, Zheng H, Feng Y, Li W. Prostate cancer diagnosis using deep learning with 3D multiparametric MRI. Presented at the Medical Imaging 2017: Computer-Aided Diagnosis; 2017.
• [126] Le MH, et al. Automated diagnosis of prostate cancer in multi-parametric MRI based on multimodal convolutional neural networks. Phys Med Biol 2017;62(16 (Jul 24)): 6497–514. http://dx.doi.org/10.1088/1361-6560/aa7731.
• [127] Fehr D, et al. Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images. Proc Natl Acad Sci U S A 2015;112(46 (Nov 17)): E6265–73.
• [128] Kumar M, Pachori RB, Acharya UR. Automated diagnosis of atrial fibrillation ECG signals using entropy features extracted from flexible analytic wavelet transform. Biocybern Biomed Eng 2018;38(3):564–73. http://dx.doi.org/10.1016/j.bbe.2018.04.004.
• [129] Cui X, Chang E, Yang W-H, Jiang B, Yang A, Peng C-K. Automated detection of paroxysmal atrial fibrillation using an information-based similarity approach. Entropy 2017;19(12):677. http://dx.doi.org/10.3390/e19120677.
• [130] Ródenas J, García M, Alcaraz R, Rieta J. Wavelet entropy automatically detects episodes of atrial fibrillation from single-lead electrocardiograms. Entropy 2015;17(9): 6179–99.
• [131] Samant P, Agarwal R. Machine learning techniques for medical diagnosis of diabetes using iris images. Comput Methods Programs Biomed 2018;157:121–8. http://dx.doi.org/10.1016/j.cmpb.2018.01.004.
• [132] Verhoeven T, et al. Automated diagnosis of temporal lobe epilepsy in the absence of interictal spikes. NeuroImage Clin 2018;17:10–5. http://dx.doi.org/10.1016/j.nicl.2017.09.021.
• [133] Focke NK, Yogarajah M, Symms MR, Gruber O, Paulus W, Duncan JS. Automated MR image classification in temporal lobe epilepsy. Neuroimage 2012;59(1):356–62.
• [134] Kamiya K, et al. Machine learning of DTI structural brain connectomes for lateralization of temporal lobe epilepsy. Magn Reson Med Sci 2015.
• [135] Yang Z, Choupan J, Reutens D, Hocking J. Lateralization of temporal lobe epilepsy based on resting-state functional magnetic resonance imaging and machine learning. Front Neurol 2015;6(184).
• [136] Rejer I, Jankowski J. EEG patterns analysis in the process of recovery from interruptions. Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015; 2016. p. 587–96.
• [137] Fix E, Hodges Jr JL. Discriminatory analysis-nonparametric discrimination: consistency properties. California Univ Berkeley; 1951.
• [138] Raghavendra U, Acharya UR, Fujita H, Gudigar A, Tan JH, Chokkadi S. Application of Gabor wavelet and locality sensitive discriminant analysis for automated identification of breast cancer using digitized mammogram images. Appl Soft Comput 2016;46:151–61. http://dx.doi.org/10.1016/j.asoc.2016.04.036.
• [139] Palaniappan R, Sundaraj K, Sundaraj S. A comparative study of the svm and k-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signals. BMC Bioinform 2014;15(1):223. http://dx.doi.org/10.1186/1471-2105-15-223.
• [140] Ibrahim S, Djemal R, Alsuwailem A. Electroencephalography (EEG) signal processing for epilepsy and autism spectrum disorder diagnosis. Biocybern Biomed Eng 2018;38(1):16–26. http://dx.doi.org/10.1016/j.bbe.2017.08.006.
• [141] Jensen A, la Cour-Harbo A. Ripples in mathematics: the discrete wavelet transform. Springer Science & Business Media; 2001.
• [142] Iasemidis LD, Sackellares JC. The evolution with time of the spatial distribution of the largest Lyapunov exponent on the human epileptic cortex. Measuring chaos in the human brain. 1991;49–82.
• [143] Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995;20(3):273–97.
• [144] Zhang C, Liu C, Zhang X, Almpanidis G. An up-to-date comparison of state-of-the-art classification algorithms. Expert Syst Appl 2017;82:128–50. http://dx.doi.org/10.1016/j.eswa.2017.04.003.
• [145] Hejazi M, Al-Haddad SAR, Singh YP, Hashim SJ, Aziz AFA. Multiclass support vector machines for classification of ECG data with missing values. Appl Artif Intell 2015;29 (7):660–74. http://dx.doi.org/10.1080/08839514.2015.1051887.
• [146] Rajesh KN, Dhuli R. Classification of ECG heartbeats using nonlinear decomposition methods and support vector machine. Comput Biol Med 2017;87:271–84. http://dx.doi.org/10.1016/j.compbiomed.2017.06.006.
• [147] Ramírez J, et al. Automatic SVM classification of sudden cardiac death and pump failure death from autonomic and repolarization ECG markers. J Electrocardiol 2015;48 (4):551–7. http://dx.doi.org/10.1016/j.jelectrocard.2015.04.002.
• [148] Gargiulo F, Fratini A, Sansone M, Sansone C. Subject identification via ECG fiducial-based systems: influence of the type of QT interval correction. Comput Methods Programs Biomed 2015;121(3):127–36. http://dx.doi.org/10.1016/j.cmpb.2015.05.012.
• [149] Ghorai S, Ghosh D. Arrhythmia classification by nonlinear kernel-based ECG signal modeling. Computer, communication and electrical technology. CRC Press; 2017. p. 325–30. http://dx.doi.org/10.1201/9781315400624-60.
• [150] Cogill S, Wang L. Support vector machine model of developmental brain gene expression data for prioritization of Autism risk gene candidates. Bioinformatics 2016;32(23):3611–8. http://dx.doi.org/10.1093/bioinformatics/btw498.
• [151] Liu Q, Gu Q, Wu Z. Feature selection method based on support vector machine and shape analysis for high-throughput medical data. Comput Biol Med 2017;91: 103–11. http://dx.doi.org/10.1016/j.compbiomed.2017.10.008.
• [152] Fondón I, et al. Automatic classification of tissue malignancy for breast carcinoma diagnosis. Comput Biol Med 2018;96:41–51. http://dx.doi.org/10.1016/j.compbiomed.2018.03.003.
• [153] Araújo T, et al. Classification of breast cancer histology images using convolutional neural networks. PloS One 2017;12(6):e0177544. http://dx.doi.org/10.1371/journal.pone.0177544.
• [154] Shen D, Wu G, Suk H-I. Deep learning in medical image analysis. Annu Rev Biomed Eng 2017;19:221–48.
• [155] Madabhushi A, Lee G. Image analysis and machine learning in digital pathology: challenges and opportunities. Elsevier; 2016.
• [156] Vu TH, Mousavi HS, Monga V, Rao G, Rao UA. Histopathological image classification using discriminative feature-oriented dictionary learning. IEEE Trans Med Imaging 2016;35(3):738–51.
• [157] Veta M, et al. Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge. Med Image Anal 2019;54(Feb 27):111–21. http://dx.doi.org/10.1016/j.media.2019.02.012.
• [158] van Diest PJ, van der Wall E, Baak JP. Prognostic value of proliferation in invasive breast cancer: a review. J Clin Pathol 2004;57(7):675–81.
• [159] Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 1979;9(1):62–6.
• [160] Kamentsky L, et al. Improved structure, function and compatibility for CellProfiler: modular high-throughput image analysis software. Bioinformatics 2011;27(8):1179–80.
• [161] He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2016. p. 770–8.
• [162] Dolatabadi AD, Khadem SEZ, Asl BM. Automated diagnosis of coronary artery disease (CAD) patients using optimized SVM. Comput Methods Programs Biomed 2017;138:117–26. http://dx.doi.org/10.1016/j.cmpb.2016.10.011.
• [163] Paiva JS, Cardoso J, Pereira T. Supervised learning methods for pathological arterial pulse wave differentiation: a SVM and neural networks approach. Int J Med Inf 2018;109:30–8. http://dx.doi.org/10.1016/j.ijmedinf.2017.10.011.
• [164] Termenon M, Graña M, Besga A, Echeveste J, Pérez J, Gonzalez-Pinto A. Diagnosis of bipolar disorder based on principal component analysis and SVM. Proceedings of the 8th International Conference on Computer Recognition Systems CORES; 2013. p. 569–78.
• [165] Huang G-B, Zhu Q-Y, Siew C-K. Extreme learning machine: theory and applications. Neurocomputing 2006;70(1-3):489–501.
• [166] Tamura SI, Tateishi M. Capabilities of a four-layered feedforward neural network: four layers versus three. IEEE Trans Neural Netw 1997;8(2):251–5.
• [167] Huang G-B. Learning capability and storage capacity of two-hidden-layer feedforward networks. IEEE Trans Neural Netw 2003;14(2):274–81.
• [168] Tang J, Deng C, Huang G-B. Extreme learning machine for multilayer perceptron. IEEE Trans Neural Netw Learn Syst 2016;27(4):809–21. http://dx.doi.org/10.1109/TNNLS.2015.2424995.
• [169] Huang G-B, Zhou H, Ding X, Zhang R. Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern B (Cybernetics) 2012;42(2):513–29.
• [170] Xia J, et al. Ultrasound-based differentiation of malignant and benign thyroid nodules: an extreme learning machine approach. Comput Methods Programs Biomed 2017;147:37–49. http://dx.doi.org/10.1016/j.cmpb.2017.06.005.
• [171] Lu S, Lu Z, Yang J, Yang M, Wang S. A pathological brain detection system based on kernel based ELM. Multimed Tools Appl 2018;77(3):3715–28.
• [172] Oung QW, Muthusamy H, Basah SN, Lee H, Vijean V. Empirical wavelet transform based features for classification of Parkinson's disease severity. J Med Syst 2018;42(2):29.
• [173] Gilles J. Empirical wavelet transform. IEEE Trans Signal Process 2013;61(16):3999–4010.
• [174] Haykin S. Neural networks. New York: Prentice Hall; 1994.
• [175] Cosma G, Brown D, Archer M, Khan M, Pockley AG. A survey on computational intelligence approaches for predictive modeling in prostate cancer. Expert Syst Appl 2017;70:1–19. http://dx.doi.org/10.1016/j.eswa.2016.11.006.
• [176] Basheer IA, Hajmeer M. Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods 2000;43(1):3–31.
• [177] Amato F, López A, Peña-Méndez EM, Vanhara P, Hampl A, Havel J. Artificial neural networks in medical diagnosis. Elsevier; 2013. http://dx.doi.org/10.2478/v10136-012-0031-x.
• [178] Walczak S, Velanovich V. Improving prognosis and reducing decision regret for pancreatic cancer treatment using artificial neural networks. Decis Support Syst 2018;106:110–8. http://dx.doi.org/10.1016/j.dss.2017.12.007.
• [179] Dalila F, Zohra A, Reda K, Hocine C. Segmentation and classification of melanoma and benign skin lesions. Optik 2017;140:749–61. http://dx.doi.org/10.1016/j.ijleo.2017.04.084.
• [180] Robnik-Šikonja M, Kononenko I. Theoretical and empirical analysis of ReliefF and RReliefF. Mach Learn 2003; 53(1-2):23–69.
• [181] Rastgoo M, Garcia R, Morel O, Marzani F. Automatic differentiation of melanoma from dysplastic nevi. Comput Med Imaging Graph 2015;43:44–52.
• [182] Kasmi R, Mokrani K. Classification of malignant melanoma and benign skin lesions: implementation of automatic ABCD rule. IET Image Process 2016;10(6):448–55. http://dx.doi.org/10.1049/iet-ipr.2015.0385.
• [183] Ferris LK, et al. Computer-aided classification of melanocytic lesions using dermoscopic images. J Am Acad Dermatol 2015;73(5):769–76. http://dx.doi.org/10.1016/j.jaad.2015.07.028.
• [184] Mroczek T. Mobile Melanoma Diagnosing System – a preliminary attempt. Human–computer systems interaction: backgrounds and applications, vol. 3. Springer; 2014. p. 213–20.
• [185] Kausu T, Gopi VP, Wahid KA, Doma W, Niwas SI. Combination of clinical and multiresolution features for glaucoma detection and its classification using fundus images. Biocybern Biomed Eng 2018;38(2):329–41. http://dx.doi.org/10.1016/j.bbe.2018.02.003.
• [186] Selesnick IW, Baraniuk RG, Kingsbury NG. The dual-tree complex wavelet transform. IEEE Signal Process Mag 2005;22(6):123–51.
• [187] Bengio Y. Learning deep architectures for AI. Found Trends® Mach Learn 2009;2(1):1–127.
• [188] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521(7553):436. http://dx.doi.org/10.1038/nature14539.
• [189] Ravì D, et al. Deep learning for health informatics. IEEE J Biomed Health Inform 2017;21(1):4–21. http://dx.doi.org/10.1109/JBHI.2016.2636665.
• [190] Tajbakhsh N, et al. Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imaging 2016;35(5):1299–312. http://dx.doi.org/10.1109/TMI.2016.2535302.
• [191] Setio AAA, et al. Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans Med Imaging 2016;35(5):1160–9. http://dx.doi.org/10.1109/TMI.2016.2536809.
• [192] Gulshan V, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016;316(22):2402–10.
• [193] van Grinsven MJ, van Ginneken B, Hoyng CB, Theelen T, Sánchez CI. Fast convolutional neural network training using selective data sampling: application to hemorrhage detection in color fundus images. IEEE Trans Med Imaging 2016;35(5):1273–84. http://dx.doi.org/10.1109/TMI.2016.2526689.
• [194] Tan JH, Acharya UR, Bhandary SV, Chua KC, Sivaprasad S. Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network. J Comput Sci 2017;20:70–9. http://dx.doi.org/10.1016/j.jocs.2017.02.006.
• [195] Hatipoglu N, Bilgin G. Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships. Med Biol Eng Comput 2017;55(10):1829–48. http://dx.doi.org/10.1007/s11517-017-1630-1.
• [196] Pereira S, Pinto A, Alves V, Silva CA. Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging 2016;35(5):1240–51. http://dx.doi.org/10.1109/TMI.2016.2538465.
• [197] Kallenberg M, et al. Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring. IEEE Trans Med Imaging 2016;35(5):1322–31. http://dx.doi.org/10.1109/TMI.2016.2532122.
• [198] Martis RJ, Acharya UR, Adeli H. Current methods in electrocardiogram characterization. Comput Biol Med 2014;48:133–49. http://dx.doi.org/10.1016/j.compbiomed.2014.02.012.
• [199] Augustyniak P. Accurate classification of ECG patterns with subject-dependent feature vector. Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015; 2016. p. 533–41.
• [200] Acharya UR, Fujita H, Oh SL, Hagiwara Y, Tan JH, Adam M. Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Inf Sci 2017;415:190–8. http://dx.doi.org/10.1016/j.ins.2017.06.027.
• [201] Lahiri T, Kumar U, Mishra H, Sarkar S, Roy AD. Analysis of ECG signal by chaos principle to help automatic diagnosis of myocardial infarction; 2009.
• [202] Arif M, Malagore IA, Afsar FA. Detection and localization of myocardial infarction using k-nearest neighbor classifier. J Med Syst 2012;36(1):279–89.
• [203] Acharya UR, et al. Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads. Knowl Based Syst 2016;99:146–56. http://dx.doi.org/10.1016/j.knosys.2016.01.040.
• [204] Sun L, Lu Y, Yang K, Li S. ECG analysis using multiple instance learning for myocardial infarction detection. IEEE Trans Biomed Eng 2012;59(12):3348–56. http://dx.doi.org/10.1109/TBME.2012.2213597.
• [205] Sharma L, Tripathy R, Dandapat S. Multiscale energy and eigenspace approach to detection and localization of myocardial infarction. IEEE Trans Biomed Eng 2015;62 (7):1827–37. http://dx.doi.org/10.1109/TBME.2015.2405134.
• [206] Safdarian N, Dabanloo NJ, Attarodi G. A new pattern recognition method for detection and localization of myocardial infarction using T-wave integral and total integral as extracted features from one cycle of ECG signal. J Biomed Sci Eng 2014;7(10):818. http://dx.doi.org/10.4236/jbise.2014.710081.
• [207] Srinivasan V, Eswaran C, Sriraam. Artificial neural network based epileptic detection using time-domain and frequency-domain features. J Med Syst 2005; 29(6):647–60.
• [208] Peker M, Sen B, Delen D. A novel method for automated diagnosis of epilepsy using complex-valued classifiers. IEEE J Biomed Health Inform 2016;20(1):108–18. http://dx.doi.org/10.1109/JBHI.2014.2387795.
• [209] Adeli H, Zhou Z, Dadmehr N. Analysis of EEG records in an epileptic patient using wavelet transform. J Neurosci Methods 2003;123(1):69–87.
• [210] Chua KC, Chandran V, Acharya UR, Lim CM. Application of higher order spectra to identify epileptic EEG. J Med Syst 2011;35(6):1563–71.
• [211] Acharya UR, Sree SV, Chattopadhyay S, Yu W, Ang PCA. Application of recurrence quantification analysis for the automated identification of epileptic EEG signals. Int J Neural Syst 2011;21(03):199–211.
• [212] Acharya UR, Sree SV, Suri JS. Automatic detection of epileptic EEG signals using higher order cumulant features. Int J Neural Syst 2011;21(05):403–14.
• [213] Bhattacharyya A, Pachori R, Upadhyay A, Acharya U. Tunable-Q wavelet transform based multiscale entropy measure for automated classification of epileptic EEG signals. Appl Sci 2017;7(4):385. http://dx.doi.org/10.3390/app7040385.
• [214] Acharya UR, Sree SV, Ang PCA, Yanti R, Suri JS. Application of non-linear and wavelet based features for the automated identification of epileptic EEG signals. Int J Neural Syst 2012;22(02):1250002.
• [215] Yu L, Chen H, Dou Q, Qin J, Heng P-A. Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Trans Med Imaging 2017;36 (4):994–1004. http://dx.doi.org/10.1109/TMI.2016.2642839.
• [216] Acharya UR, Fujita H, Lih OS, Hagiwara Y, Tan JH, Adam M. Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network. Inf Sci 2017;405:81–90. http://dx.doi.org/10.1016/j.ins.2017.04.012.
• [217] Acharya UR, et al. Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional neural network. Future Gener Comput Syst 2018;79:952–9. http://dx.doi.org/10.1016/j.future.2017.08.039.
• [218] Alonso-Atienza F, Morgado E, Fernandez-Martinez L, García-Alberola A, Rojo-Alvarez JL. Detection of life-threatening arrhythmias using feature selection and support vector machines. IEEE Trans Biomed Eng 2014;61 (3):832–40. http://dx.doi.org/10.1109/TBME.2013.2290800.
• [219] Li Q, Rajagopalan C, Clifford GD. Ventricular fibrillation and tachycardia classification using a machine learning approach. IEEE Trans Biomed Eng 2014;61(6):1607–13. http://dx.doi.org/10.1109/TBME.2013.2275000.
• [220] Tripathy R, Sharma L, Dandapat S. Detection of shockable ventricular arrhythmia using variational mode decomposition. J Med Syst 2016;40(4):79. http://dx.doi.org/10.1007/s10916-016-0441-5.
• [221] Raghavendra U, Fujita H, Bhandary SV, Gudigar A, Tan JH, Acharya UR. Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images. Inf Sci 2018;441:41–9. http://dx.doi.org/10.1016/j.ins.2018.01.051.
• [222] Maheshwari S, Pachori RB, Kanhangad V, Bhandary SV, Acharya UR. Iterative variational mode decomposition based automated detection of glaucoma using fundus images. Comput Biol Med 2017;88:142–9. http://dx.doi.org/10.1016/j.compbiomed.2017.06.017.
• [223] Maheshwari S, Pachori RB, Acharya UR. Automated diagnosis of glaucoma using empirical wavelet transform and correntropy features extracted from fundus images. IEEE J Biomed Health Inform 2017;21(3):803–13. http://dx.doi.org/10.1109/JBHI.2016.2544961.
• [224] Dua S, Acharya UR, Chowriappa P, Sree SV. Wavelet-based energy features for glaucomatous image classification. IEEE Trans Inf Technol Biomed 2012;16(1):80–7.
• [225] Krishnan MMR, Faust O. Automated glaucoma detection using hybrid feature extraction in retinal fundus images. J Mech Med Biol 2013;13(01):1350011.
• [226] Liu M, Zhang J, Adeli E, Shen D. Landmark-based deep multi-instance learning for brain disease diagnosis. Med Image Anal 2018;43:157–68. http://dx.doi.org/10.1016/j.media.2017.10.005.
• [227] Zhang J, Liu M, An L, Gao Y, Shen D. Alzheimer's disease diagnosis using landmark-based features from longitudinal structural MR images. IEEE J Biomed Health Inform 2017;21(6):1607–16. http://dx.doi.org/10.1109/JBHI.2017.2704614.
• [228] Jack Jr CR, et al. The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods. J Magn Reson Imag: an official J Int Soc Magn Reson Med 2008;27(4):685–91.
• [229] Malone IB, et al. MIRIAD—public release of a multiple time point Alzheimer's MR imaging dataset. Neuroimage 2013;70:33–6.
• [230] Bejnordi BE, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 2017;318(22):2199–210. http://dx.doi.org/10.1001/jama.2017.14585.
• [231] Swiderska-Chadaj Z, Markiewicz T, Gallego J, Bueno G, Grala B, Lorent M. Deep learning for damaged tissue detection and segmentation in Ki-67 brain tumor specimens based on the U-net model. Bull Polish Acad Sci Tech Sci 2018;66(6). http://dx.doi.org/10.24425/bpas.2018.125932.
• [232] Pedraza A, Gallego J, Lopez S, Gonzalez L, Laurinavicius A, Bueno G. Glomerulus classification with convolutional neural networks. Annual Conference on Medical Image Understanding and Analysis; 2017. pp. 839–49. http://dx.doi.org/10.1007/978-3-319-60964-573.
• [233] Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 2012;1097–105.
• [234] Ferreira C. Gene expression programming: a new adaptive algorithm for solving problems; 2001, arXiv preprint cs/ 0102027.
• [235] Yu Z, et al. A highly efficient gene expression programming (GEP) model for auxiliary diagnosis of small cell lung cancer. PloS One 2015;10(5):e0125517. http://dx.doi.org/10.1371/journal.pone.0125517.
• [236] Kusy M, Obrzut B, Kluska J. Application of gene expression programming and neural networks to predict adverse events of radical hysterectomy in cervical cancer patients. Med Biol Eng Comput 2013;51 (12):1357–65.
• [237] Zhang L, Chen J, Gao C, Liu C, Xu K. An efficient model for auxiliary diagnosis of hepatocellular carcinoma based on gene expression programming. Med Biol Eng Comput 2018;56(10):1771–9. http://dx.doi.org/10.1007/s11517-018-1811-6.
• [238] Feng F, Wu Y, Wu Y, Nie G, Ni R. The effect of artificial neural network model combined with six tumor markers in auxiliary diagnosis of lung cancer. J Med Syst 2012;36 (5):2973–80.
• [239] Sesen MB, Kadir T, Alcantara R-B, Fox J, Brady M. Survival prediction and treatment recommendation with Bayesian techniques in lung cancer. AMIA Annual Symposium Proceedings; 2012. p. 838.
• [240] Hosseinzadeh F, KayvanJoo AH, Ebrahimi M, Goliaei B. Prediction of lung tumor types based on protein attributes by machine learning algorithms. SpringerPlus 2013; 2(1):238.
• [241] Xue J-H, Titterington DM. Comment on ‘‘On discriminative vs. generative classifiers: a comparison of logistic regression and naive Bayes’’. Neural Process Lett 2008; 28(3):169.
• [242] Dreiseitl S, Ohno-Machado L, Kittler H, Vinterbo S, Billhardt H, Binder M. A comparison of machine learning methods for the diagnosis of pigmented skin lesions. J Biomed Inform 2001;34(1):28–36.
• [243] Sánchez-Monedero J, Sáez A, Pérez-Ortiz M, Gutiérrez PA, Hervás-Martínez C. Classification of melanoma presence and thickness based on computational image analysis. International Conference on Hybrid Artificial Intelligence Systems; 2016. pp. 427–38.
• [244] Ferreras A, Pablo LE, Pajarín AB, Larrosa JM, Polo V, Honrubia FM. Logistic regression analysis for early glaucoma diagnosis using optical coherence tomography. Arch Ophthalmol 2008;126(4):465–70.
• [245] Xu Y, Wang H, Zhou Q, Jiang J, Ma W, Lei X. Logistic regression analysis of contrast-enhanced ultrasound and ultrasonic elastography in differential diagnosis of thyroid nodules. Chin J Otorhinolaryngol Head Neck Surg 2013;48 (6):495–8.
• [246] Mazzocco T, Hussain A. Novel logistic regression models to aid the diagnosis of dementia. Expert Syst Appl 2012;39 (3):3356–61.
• [247] Moon WK, Chen I-L, Chang JM, Shin SU, Lo C-M, Chang R-F. The adaptive computer-aided diagnosis system based on tumor sizes for the classification of breast tumors detected at screening ultrasound. Ultrasonics 2017;76:70–7. http://dx.doi.org/10.1016/j.ultras.2016.12.017.
• [248] Fang C, et al. Gaussian discriminant analysis for optimal delineation of mild cognitive impairment in Alzheimer's disease. Int J Neural Syst. 2018;28(08):1850017. http://dx.doi.org/10.1142/S012906571850017X.
• [249] Sahin S, Tolun MR, Hassanpour R. Hybrid expert systems: a survey of current approaches and applications. Expert Syst Appl 2012;39(4):4609–17.
• [250] Straszecka E. Combining uncertainty and imprecision in models of medical diagnosis. Inf Sci 2006;176(20):3026–59.
• [251] Cosma G, Acampora G, Brown D, Rees RC, Khan M, Pockley AG. Prediction of pathological stage in patients with prostate cancer: a neuro-fuzzy model. PloS One 2016;11(6): e0155856. http://dx.doi.org/10.1371/journal.pone.0155856.
• [252] Cheng H-P, Lin Z-S, Hsiao H-F, Tseng M-L. Designing an artificial immune system-based machine learning classifier for medical diagnosis. International Conference on Information Computing and Applications; 2010. pp. 333–41.
• [253] Wang Y, Wang A-N, Ai Q, Sun H-J. An adaptive kernel-based weighted extreme learning machine approach for effective detection of Parkinson's disease. Biomed Signal Process Control 2017;38:400–10. http://dx.doi.org/10.1016/j.bspc.2017.06.015.
• [254] Nayak DR, Dash R, Majhi B. Discrete ripplet-II transform and modified PSO based improved evolutionary extreme learning machine for pathological brain detection. Neurocomputing 2018;282:232–47. http://dx.doi.org/10.1016/j.neucom.2017.12.030.
• [255] Janghorbani A, Moradi MH. Fuzzy evidential network and its application as medical prognosis and diagnosis models. J Biomed Inform 2017;72:96–107. http://dx.doi.org/10.1016/j.jbi.2017.07.004.
• [256] Nilashi M, Ibrahim O, Ahmadi H, Shahmoradi L, Farahmand M. A hybrid intelligent system for the prediction of Parkinson's disease progression using machine learning techniques. Biocybern Biomed Eng 2018;38(1):1–15. http://dx.doi.org/10.1016/j.bbe.2017.09.002.
• [257] Rascol O, Goetz C, Koller W, Poewe W, Sampaio C. Treatment interventions for Parkinson's disease: an evidence based assessment. The Lancet 2002;359 (9317):1589–98.
• [258] Tsang IW, Kwok JT, Cheung P-M. Core vector machines: fast SVM training on very large data sets. J Mach Learn Res 2005 Apr;6:363–92.
• [259] Ozcift A. SVM feature selection based rotation forest ensemble classifiers to improve computer-aided diagnosis of Parkinson disease. J Med Syst 2012;36(4):2141–7.
• [260] Chen H-L, et al. An efficient diagnosis system for detection of Parkinson's disease using fuzzy k-nearest neighbor approach. Expert Syst Appl 2013;40(1):263–71. http://dx.doi.org/10.1016/j.eswa.2012.07.014.
• [261] Babu GS, Suresh S. Parkinson's disease prediction using gene expression—a projection based learning meta- cognitive neural classifier approach. Expert Syst Appl 2013;40(5):1519–29. http://dx.doi.org/10.1016/j.eswa.2012.08.070.
• [262] Hariharan M, Polat K, Sindhu R. A new hybrid intelligent system for accurate detection of Parkinson's disease. Comput Methods Programs Biomed 2014;113(3):904–13. http://dx.doi.org/10.1016/j.cmpb.2014.01.004.
• [263] Froelich W, Wrobel K, Porwik P. Diagnosis of Parkinson's disease using speech samples and threshold-based classification. J Med Imaging Health Inform 2015;5(6):1358–63.
• [264] Buza K, Varga NÁ. ParkinsoNET: estimation of UPDRS score using hubness-aware feedforward neural networks. Appl Artif Intell 2016;30(6):541–55. http://dx.doi.org/10.1080/08839514.2016.1193716.
• [265] Al-Fatlawi AH, Jabardi MH, Ling SH. Efficient diagnosis system for Parkinson's disease using deep belief network. 2016 IEEE Congress on Evolutionary Computation (CEC); 2016. pp. 1324–30.
• [266] Behroozi M, Sami A. A multiple-classifier framework for Parkinson's disease detection based on various vocal tests. Int J Telemed Appl 2016;2016. http://dx.doi.org/10.1155/2016/6837498.
• [267] Avci D, Dogantekin A. An expert diagnosis system for Parkinson disease based on genetic algorithm-wavelet kernel-extreme learning machine. Parkinson's Dis 2016. http://dx.doi.org/10.1155/2016/5264743.
• [268] Wang H, Zheng B, Yoon SW, Ko HS. A support vector machine-based ensemble algorithm for breast cancer diagnosis. Eur J Oper Res 2018;267(2):687–99. http://dx.doi.org/10.1016/j.ejor.2017.12.001.
• [269] Kennedy J, Eberhart R. Particle swarm optimization. Proceedings of IEEE international Conference on Neural Networks, vol. 4. IEEE Press; 1995. p. 1942–8.
• [270] Mohebian MR, Marateb HR, Mansourian M, Mañanas MA, Mokarian F. A hybrid computer-aided-diagnosis system for prediction of breast cancer recurrence (HPBCR) using optimized ensemble learning. Comput Struct Biotechnol J 2017;15:75–85. http://dx.doi.org/10.1016/j.csbj.2016.11.004.
• [271] Kosko B. Fuzzy cognitive maps. Int J Man Mach Stud 1986;24(1):65–75.
• [272] Amirkhani A, Papageorgiou EI, Mohseni A, Mosavi MR. A review of fuzzy cognitive maps in medicine: taxonomy, methods, and applications. Comput Methods Programs Biomed 2017;142:129–45. http://dx.doi.org/10.1016/j.cmpb.2017.02.021.
• [273] Salmeron JL, Rahimi SA, Navali AM, Sadeghpour A. Medical diagnosis of Rheumatoid Arthritis using data driven PSO–FCM with scarce datasets. Neurocomputing 2017;232:104–12. http://dx.doi.org/10.1016/j.neucom.2016.09.113.
• [274] Leitich H, Adlassnig K-P, Kolarz G. Development and evaluation of fuzzy criteria for the diagnosis of rheumatoid arthritis. Methods Inf Med 1996;35(04/05):334–42.
• [275] Lim C, Yew K, Ng K, Abdullah B. A proposed hierarchical fuzzy inference system for the diagnosis of arthritic diseases. Australas Phys Eng Sci Med 2002;25(3):144.
• [276] Singh S, Kumar A, Panneerselvam K, Vennila JJ. Diagnosis of arthritis through fuzzy inference system. J Med Syst 2012;36(3):1459–68.
• [277] Jang J-SR, Sun C-T, Mizutani E. Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [Book Review]. IEEE Trans Automat Contr 1997;42(10):1482–4.
• [278] Ahmed SS, et al. Effect of fuzzy partitioning in Crohn's disease classification: a neuro-fuzzy-based approach. Med Biol Eng Comput 2017;55(1):101–15.
• [279] Arabasadi Z, Alizadehsani R, Roshanzamir M, Moosaei H, Yarifard AA. Computer aided decision making for heart disease detection using hybrid neural network-genetic algorithm. Comput Methods Programs Biomed 2017;141:19–26. http://dx.doi.org/10.1016/j.cmpb.2017.01.004.
• [280] Nilashi M, Ibrahim O, Ahmadi H, Shahmoradi L. A knowledge-based system for breast cancer classification using fuzzy logic method. Telemat Inform 2017;34(4): 133–44. http://dx.doi.org/10.1016/j.tele.2017.01.007.
• [281] Elter M, Schulz-Wendtland R, Wittenberg T. The prediction of breast cancer biopsy outcomes using two CAD approaches that both emphasize an intelligible decision process. Med Phys 2007;34(11):4164–72.
• [282] Yang X-S, He X. Firefly algorithm: recent advances and applications; 2013 arXiv preprintarxiv:1308.3898.
• [283] Kora P. ECG based myocardial infarction detection using hybrid firefly algorithm. Comput Methods Programs Biomed 2017;152:141–8. http://dx.doi.org/10.1016/j.cmpb.2017.09.015.
• [284] Jayachandran E. Analysis of myocardial infarction using discrete wavelet transform. J Med Syst 2010;34(6):985–92.
• [285] Lee HG, Noh KY, Ryu KH. Mining biosignal data: coronary artery disease diagnosis using linear and nonlinear features of HRV. Pacific-Asia Conference on Knowledge Discovery and Data Mining; 2007. pp. 218–28.
• [286] Giri D, et al. Automated diagnosis of coronary artery disease affected patients using LDA, PCA, ICA and discrete wavelet transform. Knowl Based Syst 2013;37:274–82.
• [287] Ranjan R, Giri V. A unified approach of ECG signal analysis. Int J Soft Comput Eng (IJSCE) 2012;2(3).
• [288] Kolodner J. Case-based reasoning. Morgan Kaufmann; 2014.
• [289] Aamodt A, Plaza E. Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun 1994;7(1):39–59.
• [290] Begum S, Ahmed MU, Funk P, Xiong N, Folke M. Case-based reasoning systems in the health sciences: a survey of recent trends and developments. IEEE Trans Syst Man Cybern C (Appl Rev) 2011;41(4):421–34.
• [291] Prentzas J, Hatzilygeroudis I. Categorizing approaches combining rule-based and case-based reasoning. Expert Syst 2007;24(2):97–122.
• [292] Riesbeck CK, Schank RC. Inside case-based reasoning. Psychology Press; 2013.
• [293] Rossille D, Laurent J-F, Burgun A. Modelling a decision-support system for oncology using rule-based and case-based reasoning methodologies. Int J Med Inf 2005; 74(2-4):299–306.
• [294] Marling C, Shubrook J, Schwartz F. Toward case-based reasoning for diabetes management: a preliminary clinical study and decision support system prototype. Comput Intell 2009;25(3):165–79.
• [295] Evans-Romaine K, Marling C. Prescribing exercise regimens for cardiac and pulmonary disease patients with CBR. Workshop on CBR in the Health Sciences at 5th International Conference on Case-based Reasoning (ICCBR-03). 2003. pp. 45–62.
• [296] Ali R, et al. Multimodal hybrid reasoning methodology for personalized wellbeing services. Comput Biol Med 2016;69:10–28. http://dx.doi.org/10.1016/j.compbiomed.2015.11.013.
• [297] Saraiva R, Perkusich M, Silva L, Almeida H, Siebra C, Perkusich A. Early diagnosis of gastrointestinal cancer by using case-based and rule-based reasoning. Expert Syst Appl 2016;61:192–202. http://dx.doi.org/10.1016/j.eswa.2016.05.026.
• [298] Sharaf-El-Deen DA, Moawad IF, Khalifa M. A new hybrid case-based reasoning approach for medical diagnosis systems. J Med Syst 2014;38(2):9. http://dx.doi.org/10.1007/s10916-014-0009-1.
• [299] Farahani FV, Ahmadi A, Zarandi MHF. Hybrid intelligent approach for diagnosis of the lung nodule from CT images using spatial kernelized fuzzy c-means and ensemble learning. Math Comput Simul 2018;149:48–68. http://dx.doi.org/10.1016/j.matcom.2018.02.001.
• [300] Jerne NK. Towards a network theory of the immune system. Ann Immunol 1974;125:373–89.
• [301] Jerne NK. The immune system. Sci Am 1973;229(1):52–63.
• [302] Zimmerman L, Vogel L, Bowden R. Understanding the vertebrate immune system: insights from the reptilian perspective. J Exp Biol 2010;213(5):661–71.
• [303] Watkins AB, Boggess LC. A resource limited artificial immune classifier. Proceedings of the 2002 Congress on Evolutionary Computation CEC'02 (Cat No 02TH8600), vol. 1. IEEE; 2002. p. 926–31.
• [304] Babu MP, Katta S. Artificial immune recognition systems in medical diagnosis. 2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS); 2015. pp. 1082–7.
• [305] Watkins A, Boggess L. A new classifier based on resource limited artificial immune systems. Proceedings of the 2002 Congress on Evolutionary Computation CEC'02 (Cat No 02TH8600), vol. 2. IEEE; 2002. p. 1546–51.
• [306] Polat K, Sahan S, Günes S. A new method to medical diagnosis: artificial immune recognition system (AIRS) with fuzzy weighted pre-processing and application to ECG arrhythmia. Expert Syst Appl 2006;31(2):264–9.
• [307] Kodaz H, Özsen S, Arslan A, Günes S. Medical application of information gain based artificial immune recognition system (AIRS): diagnosis of thyroid disease. Expert Syst Appl 2009;36(2):3086–92.
• [308] Özsen S, Günes S. Attribute weighting via genetic algorithms for attribute weighted artificial immune system (AWAIS) and its application to heart disease and liver disorders problems. Expert Syst Appl 2009;36(1): 386–92.
• [309] Polat K, Günes S. Principles component analysis, fuzzy weighting pre-processing and artificial immune recognition system based diagnostic system for diagnosis of lung cancer. Expert Syst Appl 2008;34(1):214–21.
• [310] Saybani MR, et al. Diagnosing tuberculosis with a novel support vector machine-based artificial immune recognition system. Iran Red Crescent Med J 2015;17(4). http://dx.doi.org/10.5812/ircmj.17(4)2015.24557.
• [311] Saybani MR, Wah TY, Aghabozorgi SR, Shamshirband S, Kiah MLM, Balas VE. Diagnosing breast cancer with an improved artificial immune recognition system. Soft Comput 2016;20(10):4069–84.
• [312] Liang C, Peng L. An automated diagnosis system of liver disease using artificial immune and genetic algorithms. J Med Syst 2013;37(2):9932. http://dx.doi.org/10.1007/s10916-013-9932-9.
• [313] Karthik S, Priyadarishini A, Anuradha J, Tripathy B. Classification and rule extraction using rough set for diagnosis of liver disease and its types. Adv Appl Sci Res 2011;2(3):334–45.
• [314] Garcke J, Griebel M. Data mining with sparse grids using simplicial basis functions. Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2001. p. 87–96.
• [315] Domeniconi C, Peng J, Gunopulos D. An adaptive metric machine for pattern classification. Adv Neural Inf Process Syst 2001;458–64.
• [316] Llora X, Goldberg DE, Traus I, Bernadó E. Accuracy, parsimony, and generality in evolutionary learning systems via multiobjective selection. International Workshop on Learning Classifier Systems; 2002. pp. 118–42.
• [317] Li C, et al. Developing a new intelligent system for the diagnosis of tuberculous pleural effusion. Comput Methods Programs Biomed 2018;153:211–25. http://dx.doi.org/10.1016/j.cmpb.2017.10.022.
• [318] Rawat J, Singh A, Bhadauria H, Virmani J, Devgun JS. Computer assisted classification framework for prediction of acute lymphoblastic and acute myeloblastic leukemia. Biocybern Biomed Eng 2017;37(4):637–54. http://dx.doi.org/10.1016/j.bbe.2017.07.003.
• [319] Yuan X, Xie L, Abouelenien M. A regularized ensemble framework of deep learning for cancer detection from multi-class, imbalanced training data. Pattern Recognit 2018;77:160–72. http://dx.doi.org/10.1016/j.patcog.2017.12.017.
• [320] Cheng L-C, Hu Y-H, Chiou S-H. Applying the temporal abstraction technique to the prediction of chronic kidney disease progression. J Med Syst 2017;41(5):85. http://dx.doi.org/10.1007/s10916-017-0732-5.
• [321] Liu M, Lu L, Ye X, Yu S, Salganicoff M. Sparse classification for computer aided diagnosis using learned dictionaries. International Conference on Medical Image Computing and Computer-Assisted Intervention; 2011. pp. 41–8.
• [322] Zhao W, Xu R, Hirano Y, Tachibana R, Kido S. A sparse representation based method to classify pulmonary patterns of diffuse lung diseases. Comput Math Methods Med 2015;2015. http://dx.doi.org/10.1155/2015/567932.
• [323] Eddy DM. Variations in physician practice: the role of uncertainty. Health Aff (Millwood) 1984;3(2):74–89.
• [324] Han PK, Klein WM, Arora NK. Varieties of uncertainty in health care: a conceptual taxonomy. Med Decis Making 2011;31(6):828–38.
• [325] Adlassnig K-P. Fuzzy set theory in medical diagnosis. IEEE Trans Syst Man Cybern 1986;16(2):260–5.
• [326] Ranque B, Nardon O. Medically unexplained symptoms' care in internal medicine: a paradigm of doctor-patient relationship in situation of uncertainty. La Revue de medecine interne 2017;38(7):458–66. http://dx.doi.org/10.1016/j.revmed.2016.12.005.
• [327] Haller H, Cramer H, Lauche R, Dobos G. Somatoform disorders and medically unexplained symptoms in primary care: a systematic review and meta-analysis of prevalence. Deutsches Ärzteblatt International 2015.
• [328] Nimnuan C, Hotopf M, Wessely S. Medically unexplained symptoms: an epidemiological study in seven specialities. J Psychosom Res 2001;51(1):361–7.
• [329] Brush Jr JE, Sherbino J, Norman GR. How expert clinicians intuitively recognize a medical diagnosis. Am J Med 2017;130(6):629–34. http://dx.doi.org/10.1016/j.amjmed.2017.01.045.
• [330] Barrows H, Norman G, Neufeld V, Feightner J. The clinical reasoning of randomly selected physicians in general medical practice. Clin Invest Med 1982;5(1):49–55.
• [331] Pelaccia T, et al. How and when do expert emergency physicians generate and evaluate diagnostic hypotheses? A qualitative study using head-mounted video cued-recall interviews. Ann Emerg Med 2014;64(6):575–85. http://dx.doi.org/10.1016/j.annemergmed.2014.05.003.
• [332] Cardoso D, Antunes C. Computer-aided prognosis based on temporal dependencies. 2014 IEEE 27th International Symposium on Computer-Based Medical Systems; 2014. pp. 549–50.
• [333] Zhou L, Hripcsak G. Temporal reasoning with medical data—a review with emphasis on medical natural language processing. J Biomed Inform 2007;40(2):183–202.
• [334] Yoon J, Zame WR, van der Schaar M. Estimating missing data in temporal data streams using multi-directional recurrent neural networks. IEEE Trans Biomed Eng 2019;66:1477–90. http://dx.doi.org/10.1109/TBME.2018.2874712.
• [335] Madkour M, Benhaddou D, Tao C. Temporal data representation, normalization, extraction, and reasoning: a review from clinical domain. Comput Methods Programs Biomed 2016;128:52–68. http://dx.doi.org/10.1016/j.cmpb.2016.02.007.
• [336] Bates MJ. Understanding information retrieval systems. Boca Raton, USA: Taylor & Francis Group; 2012.
• [337] Fan J, Li R. Statistical challenges with high dimensionality: feature selection in knowledge discovery; 2006, arXiv preprint math/0602133.
• [338] Yamada M, et al. Ultra high-dimensional nonlinear feature selection for big biological data. IEEE Trans Knowl Data Eng 2018;30(7):1352–65. http://dx.doi.org/10.1109/TKDE.2018.2789451.
• [339] Kalina J. Classification methods for high-dimensional genetic data. Biocybern Biomed Eng 2014;34(1):10–8. http://dx.doi.org/10.1016/j.bbe.2013.09.007.
• [340] Guessoum S, Laskri MT, Lieber J. RespiDiag: a case-based reasoning system for the diagnosis of chronic obstructive pulmonary disease. Expert Syst Appl 2014;41(2):267–73. http://dx.doi.org/10.1016/j.eswa.2013.05.065.
• [341] Ichise R, Numao M. Learning first-order rules to handle medical data. NII J 2001;3(2):9–14.
• [342] Yoo I, et al. Data mining in healthcare and biomedicine: a survey of the literature. J Med Syst 2012;36(4):2431–48.
• [343] Prather JC, Lobach DF, Goodwin LK, Hales JW, Hage ML, Hammond WE. Medical data mining: knowledge discovery in a clinical data warehouse. Proceedings of the AMIA Annual Fall symposium; 1997. p. 101.
• [344] Krishna R, Kelleher K, Stahlberg E. Patient confidentiality in the research use of clinical medical databases. Am J Public Health 2007;97(4):654–8.
• [345] Berger AM, Berger CR. Data mining as a tool for research and knowledge development in nursing. CIN: Comp Inform Nurs (Lond) 2004;22(3):123–31.
• [346] SoRelle R. Reducing the rate of medical errors in the United States. Circulation 2000;101(3):e39–40.
• [347] Varachiu N, Karanicolas C, Ulieru M. Computational intelligence for medical knowledge acquisition with application to glaucoma. Proceedings First IEEE International Conference on Cognitive Informatics; 2002. p. 233–8.
• [348] Hernández-Chan G, Rodríguez-González A, Alor- Hernández G, Gómez-Berbís J, Mayer-Pujadas M, Posada- Gómez R. Knowledge acquisition for medical diagnosis using collective intelligence. J Med Syst 2012;36(1):5–9.
• [349] Boegl K, Adlassnig K-P, Hayashi Y, Rothenfluh TE, Leitich H. Knowledge acquisition in the fuzzy knowledge representation framework of a medical consultation system. Artif Intell Med 2004;30(1):1–26.
• [350] Kononenko I. Machine learning for medical diagnosis: history, state of the art and perspective. Artif Intell Med 2001;23(1):89–109.
• [351] Bellazzi R, Zupan B. Predictive data mining in clinical medicine: current issues and guidelines. Int J Med Inf 2008;77(2):81–97.
• [352] Ahmed MU, Begum S, Funk P. A hybrid case-based system in stress diagnosis and treatment. IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI2012); 2012.
• [353] Lipton ZC. The mythos of model interpretability; 2016 arXiv preprintarxiv:1606.03490.
• [354] Holte RC. Very simple classification rules perform well on most commonly used datasets. Mach Learn 1993;11(1): 63–90.
• [355] Schmidt C. Capsule endoscopy to screen for colon cancer scores low on sensitivity, high on controversy. Oxford University Press; 2009.
• [356] He H, Garcia EA. Learning from imbalanced data. IEEE Trans Knowl Data Eng 2008;9:1263–84.
• [357] Weiss GM. Mining with rarity: a unifying framework. ACM Sigkdd Explorations Newsletter 2004;6(1):7–19.
• [358] Chawla NV, Japkowicz N, Kotcz A. Special issue on learning from imbalanced data sets. ACM Sigkdd Explorations Newsletter 2004;6(1):1–6.
• [359] Yu H, Ni J, Zhao J. ACOSampling: an ant colony optimization-based undersampling method for classifying imbalanced DNA microarray data. Neurocomputing 2013;101:309–18. http://dx.doi.org/10.1016/j.neucom.2012.08.018.
• [360] Wigle DA, et al. Molecular profiling of non-small cell lung cancer and correlation with disease-free survival. Cancer Res 2002;62(11):3005–8.
• [361] Alon U, et al. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci 1999;96(12):6745–50.
• [362] Nutt CL, et al. Gene expression-based classification of malignant gliomas correlates better with survival than histological classification. Cancer Res 2003;63(7):1602–7.
• [363] Pomeroy SL, et al. Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature 2002;415(6870):436.
• [364] Han H, Wang W-Y, Mao B-H. Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. International Conference on Intelligent Computing; 2005. pp. 878–87.
• [365] Wang S, Li Z, Chao W, Cao Q. Applying adaptive over-sampling technique based on data density and cost-sensitive SVM to imbalanced learning. The 2012 International Joint Conference on Neural Networks (IJCNN); 2012. pp. 1–8.
• [366] Freund Y, Schapire RE. A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 1997;55(1):119–39.
• [367] Schapire RE, Singer Y. Improved boosting algorithms using confidence-rated predictions. Mach Learn 1999;37(3):297–336.
• [368] Mukherjee I, Schapire RE. A theory of multiclass boosting. J Mach Learn Res 2013;14(Feb):437–97.
• [369] Saberian MJ, Vasconcelos N. Multiclass boosting: theory and algorithms. Adv Neural Inf Process Syst 2011;2124–32.
• [370] Codella NC, et al. Deep learning ensembles for melanoma recognition in dermoscopy images. IBM J Res Dev 2017;61 (4/5). http://dx.doi.org/10.1147/JRD.2017.2708299. pp. 5 1–5: 15.
• [371] Xiao Y, Wu J, Lin Z, Zhao X. A deep learning-based multi- model ensemble method for cancer prediction. Comput Methods Programs Biomed 2018;153:1–9. http://dx.doi.org/10.1016/j.cmpb.2017.09.005.
• [372] Seiffert C, Khoshgoftaar TM, Van Hulse J, Napolitano A. RUSBoost: a hybrid approach to alleviating class imbalance. IEEE Trans Syst Man Cybern A: Syst Hum 2010;40(1):185–97.
• [373] Chawla NV, Lazarevic A, Hall LO, Bowyer KW. SMOTEBoost: improving prediction of the minority class in boosting. European Conference on Principles of Data Mining and Knowledge Discovery; 2003. pp. 107–19.
• [374] Wang S, Yao X. Multiclass imbalance problems: analysis and potential solutions. IEEE Trans Syst Man Cybern B (Cybernetics) 2012;42(4):1119–30.
• [375] Lieder F, Griffiths T. Resource-rational analysis: understanding human cognition as the optimal use of limited computational resources. Behav Brain Sci 2019. http://dx.doi.org/10.1017/S0140525X061X.
• [376] Odenbaugh J. True lies: realism, robustness, and models. Philos Sci 2011;78(5):1177–88.
• [377] O'neill R, Temple R. The prevention and treatment of missing data in clinical trials: an FDA perspective on the importance of dealing with it. Clin Pharmacol Therap 2012;91(3):550–4.
• [378] Lewis JA. Statistical principles for clinical trials (ICH E9): an introductory note on an international guideline. Stat Med 1999;18(15):1903–42.
• [379] Chen Y-C, Zhang J. Guideline on missing data in confirmatory clinical trials. Chin J New Drugs 2012;7:10.
• [380] Kang H. The prevention and handling of the missing data. Korean J Anesthesiol 2013;64(5):402. http://dx.doi.org/10.4097/kjae.2013.64.5.402.
• [381] Wells BJ, Chagin KM, Nowacki AS, Kattan MW. Strategies for handling missing data in electronic health record derived data. EGEMS 2013;1(3). http://dx.doi.org/10.13063/2327-9214.1035.
• [382] Tsai C-F, Li M-L, Lin W-C. A class center based approach for missing value imputation. Knowl Based Syst 2018;151:124–35. http://dx.doi.org/10.1016/j.knosys.2018.03.026.
• [383] Belanche LA, Kobayashi V, Aluja T. Handling missing values in kernel methods with application to microbiology data. Neurocomputing 2014;141:110–6. http://dx.doi.org/10.1016/j.neucom.2014.01.047.
• [384] Grzymala-Busse JW, Goodwin LK, Grzymala-Busse WJ, Zheng X. Handling missing attribute values in preterm birth data sets. International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing; 2005. pp. 342–51.
• [385] Purwar A, Singh SK. Hybrid prediction model with missing value imputation for medical data. Expert Syst Appl 2015;42(13):5621–31. http://dx.doi.org/10.1016/j.eswa.2015.02.050.
• [386] Pesonen E, Eskelinen M, Juhola M. Treatment of missing data values in a neural network based decision support system for acute abdominal pain. Artif Intell Med 1998;13 (3):139–46.
• [387] Gamberger D, Lavrac N, Groselj C. Experiments with noise filtering in a medical domain. ICML 1999;143–51.
• [388] Pawlak Z. Rough set theory and its applications to data analysis. Cybern Syst 1998;29(7):661–88.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).