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Tytuł artykułu

Assessment measures of an ensemble classifier based on the distributivity equation to predict the presence of severe coronary artery disease

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Języki publikacji
EN
Abstrakty
EN
The aim of this study is to apply and evaluate the usefulness of the hybrid classifier to predict the presence of serious coronary artery disease based on clinical data and 24-hour Holter ECG monitoring. Our approach relies on an ensemble classifier applying the distributivity equation aggregating base classifiers accordingly. Such a method may be helpful for physicians in the management of patients with coronary artery disease, in particular in the face of limited access to invasive diagnostic tests, i.e., coronary angiography, or in the case of contraindications to its performance. The paper includes results of experiments performed on medical data obtained from the Department of Internal Medicine, Jagiellonian University Medical College, Kraków, Poland. The data set contains clinical data, data from Holter ECG (24-hour ECG monitoring), and coronary angiography. A leave-one-out cross-validation technique is used for the performance evaluation of the classifiers on a data set using the WEKA (Waikato Environment for Knowledge Analysis) tool. We present the results of comparing our hybrid algorithm created from aggregation with the distributive equation of selected classification algorithms (multilayer perceptron network, support vector machine, k-nearest neighbors, naïve Bayes, and random forests) with themselves on raw data.
Rocznik
Strony
361--377
Opis fizyczny
Bibliogr. 35 poz., rys., tab., wykr.
Twórcy
autor
  • Institute of Mathematics, University of Rzeszów, Pigonia 1, 35-310 Rzeszów, Poland
autor
  • Institute of Computer Science, University of Rzeszów, Pigonia 1, 35-310 Rzeszów, Poland
autor
  • Institute of Computer Science, University of Rzeszów, Pigonia 1, 35-310 Rzeszów, Poland
  • Department of Internal Medicine, Jagiellonian University Medical College, Skawinska 8, 31-066 Kraków, Poland
Bibliografia
  • [1] Aczél, J. (1966). Lectures on Functional Equations and Their Applications, Academic Press, New York/London.
  • [2] Akay, M. (1992). Noninvasive diagnosis of coronary artery disease using a neural network algorithm, Biological Cybernetics 67(4): 361-367.
  • [3] Alfaidi, A., Aljuhani, R., Alshehri, B., Alwadei, H. and Sabbeh, S. (2022). Machine learning: Assisted cardiovascular diseases diagnosis, International Journal of Advanced Computer Science and Applications 13(2): 135-141.
  • [4] Alizadehsani, R., Abdar, M., Roshanzamir, M., Khosravi, A., Kebria, P.M., Khozeimeh, F., Nahavandi, S., Sarrafzadegan, N., Acharya, U.R. and Abdar, M. (2019). Machine learning-based coronary artery disease diagnosis: A comprehensive review, Computers in Biology and Medicine 111(4): 103346.
  • [5] Alizadehsani, R., Habibi, J., Alizadeh-Sani, Z., Mashayekhi, H., Boghrati, R., Ghandeharioun, A., Khozeimeh, F. and Alizadeh-Sani, F. (2013). Diagnosing coronary artery disease via data mining algorithms by considering laboratory and echocardiography features, Research in Cardiovascular Medicine 2(3): 133-139.
  • [6] Alizadehsani, R., Zangooei, M.H., Hosseini, M.J., Habibi, J., Khosravi, A., Roshanzamir, M., Khozeimeh, F., Sarrafzadegan, N. and Nahavandi, S. (2016). Coronary artery disease detection using computational intelligence methods, Knowledge-Based Systems 109: 187-197.
  • [7] Babaoglu, I., Baykan, O.K., Aygul, N., Ozdemir, K. and Bayrak, M. (2009). Assessment of exercise stress testing with artificial neural network in determining coronary artery disease and predicting lesion localization, Expert Systems with Applications 36(2): 2562-2566.
  • [8] Bazan, J.G., Bazan-Socha, S., Ochaba, M., Buregwa-Czuma, S., Nowakowski, T. and Woźniak, M. (2020). Effective construction of classifiers with the k-NN method supported by a concept ontology, Knowledge and Information Systems 62: 1497-1510.
  • [9] Beliakov, G., Bustince, H. and Calvo, T. (2016). A Practical Guide to Averaging Functions, Springer, Cham.
  • [10] Bernardo, L.C., Damaševičius, R., de Albuquerque, V.H.C. and Maskeliūnas, R. (2021). A hybrid two-stage SqueezeNet and support vector machine system for Parkinson’s disease detection based on handwritten spiral patterns, International Journal of Applied Mathematics and Computer Science 31(4): 549-561, DOI: 10.34768/amcs-2021-0037.
  • [11] Castaneda, C., Nalley, K., Mannion, C., Bhattacharyya, P., Blake, P., Pecora, A., Goy, A. and Suh, K.S. (2015). Clinical decision support systems for improving diagnostic accuracy and achieving precision medicine, Journal of Clinical Bioinformatics 5(4): 1-16.
  • [12] Dietterich, T.G. (2000). Ensemble methods in machine learning, in D. Haussler (Ed.), Multiple Classifier Systems, Springer, Heidelberg, pp. 1-12.
  • [13] Dombi, J. (1982). Basic concepts for the theory of evaluation: The aggregative operator, European Journal of Operational Research 10(3): 282-293.
  • [14] Drewniak, J., Drygaś, P. and Rak, E. (2008). Distributivity equations for uninorms and nullnorms, Fuzzy Sets and Systems 159(13): 1646-1657.
  • [15] Drewniak, J. and Rak, E. (2010). Subdistributivity and superdistributivity of binary operations, Fuzzy Sets and Systems 161(2): 189-201.
  • [16] Du, K.-L. and Swamy, M.N.S. (2014). Neural Networks and Statistical Learning, Springer, London.
  • [17] Dua, D. and Casey, G. (2019). UC Irvine Machine Learning Repository, http://archive.ics.uci.edu/ml/.
  • [18] Frank, E., Hall, M.A. and Witten, I.H. (2016). The WEKA Workbench-Online Appendix for Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, Burlington.
  • [19] Garg, H. (2021). New exponential operation laws and operators for interval-valued q-rung orthopair fuzzy sets in group decision-making process, Neural Computing and Applications 33: 13937-13963, DOI: 10.1007/s00521-021-06036-0.
  • [20] Grabisch, M., Marichal, J.L., Mesiar, R. and Pap, E. (2009). Aggregation Functions, Cambridge University Press, Cambridge.
  • [21] Jaworski, M., Duda, P. and Rutkowski, L. (2018). New splitting criteria for decision trees in stationary data streams, IEEE Transactions on Neural Networks and Learning Systems 29(6): 2516-2529.
  • [22] Kaczmarek-Majer, K. and Kiersztyn, A. (2022). Experimental evaluation of the accuracy of an ensemble of fuzzy methods for classification of episodes in bipolar disorder, IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2022), Padua, Italy, pp. 1-7.
  • [23] Kim, Y.S. (2008). Comparision of the decision tree, artificial neural network, and linear regression methods based on the number and types of independent variables and sample size, Journal of Expert Systems with Application 34(2): 1227-1234.
  • [24] Klement, E.P., Mesiar, R. and Pap, E. (2000). Triangular Norms, Kluwer Academic Publishers, Dordrecht.
  • [25] Kowal, M., Skobel, M., Gramacki, A. and Korbicz, J. (2021). Breast cancer nuclei segmentation and classification based on a deep learning approach, International Journal of Applied Mathematics and Computer Science 31(1): 85-106, DOI: 10.34768/amcs-2021-0007.
  • [26] Krittanawong, C., Virk, H.U.H., Bangalore, S., Wang, Z., Johnson, K.W., Pinotti, R., Zhang, H., Kaplin, S., Narasimhan, B., Kitai, T., Baber, U., Halperin, J.L. and Tang, W.H.W. (2020). Machine learning prediction in cardiovascular diseases: A meta-analysis, Scientific Reports 10, Article no. 16057.
  • [27] Kunapuli, G. (2023). Ensemble Methods for Machine Learning, Manning Publications Co, New York.
  • [28] Moody, G.B. and Jager, F. (2003). Distinguishing ischemic from non-ischemic ST changes: The PhysioNet/Computers in Cardiology Challenge 2003, Computers in Cardiology, Thessaloniki, Greece, pp. 235-237, DOI: 10.1109/CIC.2003.1291134.
  • [29] Patro, K.K., Prakash, A.J., Samantray, S., Pławiak, J., Tadeusiewicz, R. and Pławiak, P. (2022). A hybrid approach of a deep learning technique for real-time ECG beat detection, International Journal of Applied Mathematics and Computer Science 32(3): 455-465, DOI: 10.34768/amcs-2022-0033.
  • [30] Rak, E., Bazan, J.G., Szczur, A. and Rząsa, W. (2020). The distributivity law as a tool of k-NN classifiers’ aggregation: Mining a cyber-attack data set, IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2020), Glasgow, UK, pp. 1-8.
  • [31] Rak, E. and Szczur, A. (2021). A comparative assessment of aggregated classification algorithms with the use to mining a cyber-attack data set, IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2021), Luxembourg, pp. 1-6.
  • [32] Schmidhuber, J. (2015). Deep learning in neural networks: An overview, Neural Network 61: 85-117.
  • [33] Tanveer, M., Gautam, C. and Suganthan, P.N. (2019). Comprehensive evaluation of twin SVM based classifiers on UCI datasets, Applied Soft Computing 83: 105617.
  • [34] Zhang, S., Cheng, D., Deng, Z., Zong, M. and Deng, X. (2018). A novel kNN algorithm with data-driven k parameter computation, Pattern Recognition Letters 109: 44-54.
  • [35] Zipes, D.P., Libby, P., Bonow, R.O., Mann, D.L. and Tomaselli, G.F. (2018). Braunwald’s Heart Disease E-Book: A Text-book of Cardiovascular Medicine, Elsevier, Amsterdam.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f8ef6564-a825-4cd3-8a4b-a8447bdf0ea8
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