Acceso abierto

Assessment Measures of an Ensemble Classifier Based on the Distributivity Equation to Predict the Presence of Severe Coronary Artery Disease

International Journal of Applied Mathematics and Computer Science's Cover Image
International Journal of Applied Mathematics and Computer Science
Mathematical Modeling in Medical Problems (Special section, pp. 349-428), Urszula Foryś, Katarzyna Rejniak, Barbara Pękala, Agnieszka Bartłomiejczyk (Eds.)

Cite

Aczél, J. (1966). Lectures on Functional Equations and Their Applications, Academic Press, New York/London. Search in Google Scholar

Akay, M. (1992). Noninvasive diagnosis of coronary artery disease using a neural network algorithm, Biological Cybernetics 67(4): 361–367. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

Beliakov, G., Bustince, H. and Calvo, T. (2016). A Practical Guide to Averaging Functions, Springer, Cham. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

Dietterich, T.G. (2000). Ensemble methods in machine learning, in D. Haussler (Ed.), Multiple Classifier Systems, Springer, Heidelberg, pp. 1–12. Search in Google Scholar

Dombi, J. (1982). Basic concepts for the theory of evaluation: The aggregative operator, European Journal of Operational Research 10(3): 282–293. Search in Google Scholar

Drewniak, J., Drygaś, P. and Rak, E. (2008). Distributivity equations for uninorms and nullnorms, Fuzzy Sets and Systems 159(13): 1646–1657. Search in Google Scholar

Drewniak, J. and Rak, E. (2010). Subdistributivity and superdistributivity of binary operations, Fuzzy Sets and Systems 161(2): 189–201. Search in Google Scholar

Du, K.-L. and Swamy, M.N.S. (2014). Neural Networks and Statistical Learning, Springer, London. Search in Google Scholar

Dua, D. and Casey, G. (2019). UC Irvine Machine Learning Repository, http://archive.ics.uci.edu/ml/. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

Grabisch, M., Marichal, J.L., Mesiar, R. and Pap, E. (2009). Aggregation Functions, Cambridge University Press, Cambridge. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

Klement, E.P., Mesiar, R. and Pap, E. (2000). Triangular Norms, Kluwer Academic Publishers, Dordrecht. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

Kunapuli, G. (2023). Ensemble Methods for Machine Learning, Manning Publications Co, New York. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

Rak, E., Bazan, J.G., Szczur, A. and Rzaşa, 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. Search in Google Scholar

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. Search in Google Scholar

Schmidhuber, J. (2015). Deep learning in neural networks: An overview, Neural Network 61: 85–117. Search in Google Scholar

Tanveer, M., Gautam, C. and Suganthan, P.N. (2019). Comprehensive evaluation of twin SVM based classifiers on UCI datasets, Applied Soft Computing 83: 105617. Search in Google Scholar

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. Search in Google Scholar

Zipes, D.P., Libby, P., Bonow, R.O., Mann, D.L. and Tomaselli, G.F. (2018). Braunwald’s Heart Disease E-Book: A Textbook of Cardiovascular Medicine, Elsevier, Amsterdam. Search in Google Scholar

eISSN:
2083-8492
Idioma:
Inglés
Calendario de la edición:
4 veces al año
Temas de la revista:
Mathematics, Applied Mathematics