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Języki publikacji
Abstrakty
Wart disease is a kind of skin illness that is caused by Human Papillomavirus (HPV). Many medical studies are being carried out with the aid of machine learning and data mining techniques to find the most appropriate and effective treatment for a specific wart patient. However, the imbalanced distribution of medical data may lead to misclassification in this field. The purpose of this paper is to propose a algorithm to predict the response of the patients towards a specific treatment and choose an appropriate treatment method. In this paper, Synthetic Minority Over-sampling (SMOTE) method is adopted to deal with the unbalanced data and combined with Axiomatic Fuzzy Set (AFS) theory to predict whether patients can respond to treatment or not. Compared with other existing approaches, the proposed approach can provide descriptive information of the patients which can help to predict the response towards the treatment with an average prediction accuracy of 97.63% and 92.33% for cryotherapy and immunotherapy data, respectively. Furthermore, the ex-perimental results demonstrate that it can assist doctors in treatment, save medical resources and improve the quality of treatment.
Wydawca
Czasopismo
Rocznik
Tom
Strony
517--526
Opis fizyczny
Bibliogr. 36 poz., rys., tab., wykr.
Twórcy
autor
- School of Control Science and Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, People's Republic of China
autor
- School of Control Science and Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, People's Republic of China
autor
- School of Bioengineering, Dalian University of Technology, Dalian, People's Republic of China
autor
- School of Control Science and Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, People's Republic of China
autor
- School of Control Science and Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, People's Republic of China
Bibliografia
- [1] Witten IH, Frank E, Hall MA, Pal CJ. Data mining: practical machine learning tools and techniques. 4th ed. Burlington, MA: Morgan Kaufmann; 2016.
- [2] Saarela M, Ryynänen OP, Äyrämö S. Predicting hospital associated disability from imbalanced data using supervised learning. Artif Intell Med 2019;95:88–95.
- [3] Khozeimeh F, Jabbari Azad F, Mahboubi Oskouei Y, Jafari M, Tehranian S, Alizadehsani R, et al. Intralesional immunotherapy compared to cryotherapy in the treatment of warts. Int J Dermatol 2017;56(4):474–8.
- [4] Khozeimeh F, Alizadehsani R, Roshanzamir M, Khosravi A, Layegh P, Nahavandi S. An expert system for selecting wart treatment method. Comput Biol Med 2017;81:167–75.
- [5] Akben SB. Predicting the success of wart treatment methods using decision tree based fuzzy informative images. Biocybern Biomed Eng 2018;38(4):819–27.
- [6] Uzun R, Isler Y, Toksan M. Choose of wart treatment method using naive bayes and k-nearest neighbors classifiers. The 26th Signal Processing and Communications Applications Conference (SIU). 2018. pp. 1–4.
- [7] Ghiasi MM, Zendehboudi S. Decision tree-based methodology to select a proper approach for wart treatment. Comput Biol Med 2019;108:400–9.
- [8] Abdar M, Wijayaningrum VN, Hussain S, Alizadehsani R, Plawiak P, Acharya UR, et al. IAPSO-AIRS: a novel improved machine learning-based system for wart disease treatment. J Med Syst 2019;43(7):220.
- [9] Guimarães A, Campos Souza P, Araujo V, Rezende T, Araujo V. Pruning fuzzy neural network applied to the construction of expert systems to aid in the diagnosis of the treatment of cryotherapy and immunotherapy. Big Data Cognit Comput 2019;3:22.
- [10] Han W, Huang Z, Li S, Jia Y. Distribution-sensitive un-balanced data oversampling method for medical diagnosis. J Med Syst 2019;43(2):39.
- [11] Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: Synthetic minority over-sampling technique. J Artif Intell Res 2002;16(1):321–57.
- [12] Duan X, Wang Y, Pedrycz W, Liu X, Wang C, Li Z. AFSNN: a classification algorithm using axiomatic fuzzy sets and neural networks. IEEE Trans Fuzzy Syst 2018;26(5):3151–63.
- [13] Liu X. The fuzzy sets and systems based on AFS structure, EI algebra and EII algebra. Fuzzy Sets Syst 1998;95(2):179–88.
- [14] Liu X, Jia W, Wang Y, Guo H, Ren Y, Li Z. Knowledge discovery and semantic learning in the framework of axiomatic fuzzy set theory. WIREs Data Min Knowl Discov 2018;8(5):e1268.
- [15] Dheeru D, Karra Taniskidou E. UCI machine learning repository; 2017, http://archive.ics.uci.edu/ml.
- [16] Chen D, Wang X, Zhou C, Wang B. The distance-based balancing ensemble method for data with a high imbalance ratio. IEEE Access 2019;7:68940–56.
- [17] Ha TM, Bunke H. Off-line, handwritten numeral recognition by perturbation method. IEEE Trans Pattern Anal Mach Intell 1997;19(5):535–9.
- [18] Liu X, Pedrycz W. Axiomatic fuzzy set theory and its applications. Heidelberg, Germany: Springer-Verlag; 2009.
- [19] Liu X. The fuzzy theory based on afs algebras and afs structure. J Math Anal Appl 1998;217(2):459–78.
- [20] Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in Python. J Mach Learn Res 2011;12:2825–30.
- [21] Liu X, Feng X, Pedrycz W. Extraction of fuzzy rules from fuzzy decision trees: An axiomatic fuzzy sets (AFS) approach. Data Knowl Eng 2013;84:1–25.
- [22] Lemaître G, Nogueira F, Aridas CK. Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning. J Mach Learn Res 2017;18(17):1–5.
- [23] Goldberger J, Roweis ST, Hinton GE, Salakhutdinov R. Neighbourhood components analysis. Adv Neural Inf Process Syst 2004;17(6):513–20.
- [24] Chang CC, Lin CJ. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2011. 2:27:1–27:27. Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm.
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- [26] Breiman L, Friedman J, Olshen R, Stone C. Classification and regression trees. Monterey, CA: Wadsworth and Brooks; 1984.
- [27] Breiman L. Random forests. Mach Learn 2001;45(1):5–32.
- [28] Rojas R, Feldman J. Neural networks: a systematic introduction. New-York: Springer-Verlag and Berlin; 1996.
- [29] 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.
- [30] Zhang H. Choose of wart treatment method using naive bayes and k-nearest neighbors classifiers. The Seventeenth International Florida Artificial Intelligence Research Society Conference (FLAIRS). 2004. pp. 1–6.
- [31] Christou V, Tsipouras MG, Giannakeas N, Tzallas AT, Brown G. Hybrid extreme learning machine approach for heterogeneous neural networks. Neurocomputing 2019;361:137–50.
- [32] Rahman M, Wang S, Zhou Y, Rogers J. Predicting the performance of cryotherapy for wart treatment using machine learning algorithms. 2019 IISE Annual Conference; 2019.
- [33] Alizadehsani R, Abdar M, Jalali SMJ, Roshanzamir M, Khosravi A, Nahavandi S. Predicting the performance of cryotherapy for wart treatment using machine learning algorithms. The International Workshop on Future Technology. 2019. pp. 6–18.
- [34] Gavazzi G, Krause KH. Ageing and infection. Lancet Infect Dis 2002;2(11):659–66.
- [35] Tomson N, Sterling J, Ahmed I, Hague J, Berth-Jones J. Human papillomavirus typing of warts and response to cryotherapy. J Eur Acad Dermatol Venereol 2011;25 (9):1108–11.
- [36] Berth-Jones J, Hutchinson PE. Modern treatment of warts: cure rates at 3 and 6 months. Br J Dermatol 1992;127 (3):262–5.
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-03e132d9-5889-4a8e-80cd-5f497be2b594