Comparison of machine learning methods for the classification of cardiovascular disease

Informatics in Medicine Unlocked - Tập 24 - Trang 100606 - 2021
Rachael Hagan1, Charles J. Gillan1, Fiona Mallett1
1School of Electrical and Electronic Engineering and Computer Science, Queen's University Belfast, Queen's Road, Queen's Island, Belfast, Northern Ireland, BT9 3DT, United Kingdom

Tài liệu tham khảo

Frost, 2016 E Topal, Deep medicine: how artificial intelligence can make healthcare human again (first ed.), Pub: Basic Books, ISBN-13: 978-1541644632. Feldman, 1983, Computer detection of cardiac arrhythmia: historical review, Am. Rev. Diagn., 2, 138 Sharma, 2017, QRS complex detection in ECG signals using locally adaptive weighted total variation denoising, Comput Biol Med, 87, 187, 10.1016/j.compbiomed.2017.05.027 Ji Hwan Park, Han Eol Cho, Jong Hun Kim, Melanie Wall, Yaakov Stern, Hyunsun Lim, Shinjae Yoo, Hyoung-Seop Kim and Jiook Cha, Electronic health records based prediction of future incidence of alzheimers disease using machine learning. 10.1101/625582. DARPA Project Explainable Artificial Intelligence (XAI). Project web site: https://www.darpa.mil/program/explainable-artificial-intelligence. Doran, 2017, What does explainable AI really mean? A new conceptualization of perspectives Guvenir, 1997, Supervised machine learning algorithm for arrythima analysis, 433 UCI machine learning repository; Irvine California, USA. University of California, School of Information and Computer Science, available on the web at: http://archive.ics.uci.edu/ml. Branco, 2016, A survey of predictive modeling on imbalanced domains, ACM Comput Surv, 49, 1, 10.1145/2907070 S Ulianova, The cardiovascular disease dataset. available on the web at: https://www.kaggle.com/sulianova/cardiovascular-disease-dataset. Kuhn, 2013 Maiga, 2019, Comparison of machine learning models in prediction of cardiovascular disease using health record data, 45 Chauhan, 2020, Cardiovascular disease prediction using classification algorithms of machine learning, Int J Sci Res, 9, 194 Friedman J H, Greedy function approximation, The IMS 1999 Ritz Lecture available on the web at: http://statweb.stanford.edu/~jhf/ftp/trebst.pdf. Bell, 2015 J. Mercer, Functions of positive and negative type and their connection with the theory of integral equations, Philosophical Transactions of the Royal Society A, 209, 415–446. Sharma, 2020, Activation functions in neural networks, Int. J. Eng. Appl. Sci. Technol., 4, 310 Darling, 2018 Chang, 2011, LIBSVM : a library for support vector machines, ACM Trans Intell Syst Technol, 2, 10.1145/1961189.1961199 Hsu, 2002, A comparison of methods for multi-class support vector machines, IEEE Trans Neural Network, 13, 415, 10.1109/72.991427 Winters-Hilt, 2006, Support vector machine implementations for classification and clustering, BMC Bioinf, 7, S4, 10.1186/1471-2105-7-S2-S4 Kajan, 2014 Aliferis, 2003, HITON: a novel Markov blanket algorithm for optimal variable selection, 21 McGregor, 2013, Big data in neonatal intensive care, IEEE J Mag, 46, 54 Rahman, 2015, Utilizing ECG-based heartbeat classification for hypertrophic cardiomyopathy identification, IEEE Trans Nanobiosci, 14, 505, 10.1109/TNB.2015.2426213 Venkatesan, 2018, ECG signal preprocessing and SVM classifier-based abnormality detection in Remote healthcare applications, IEEE Access, 6, 9767, 10.1109/ACCESS.2018.2794346 Slavomír, 2015, Application of neural network in medical diagnostics Acharya, 2017, A deep convolutional neural network model to classify heartbeats, J. Comput. Biol. Med., 10.1016/j.compbiomed.2017.08.022 Rajendra Acharya, 2018, Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals, J. Comp Bio Med, 100, 270, 10.1016/j.compbiomed.2017.09.017