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Cardiovascular disease (CVD) is an important factor in life since it may cause the death of human by effecting the heart and blood vessels of the body. Early detection of this disease is necessary for securing patients life. For this purpose, an automated tool is proposed in this paper for detecting patients with CVD and assisting health care systems also. A stacked-GRU based Recurrent Neural Network model, abbreviated as, stk-G, is proposed in this paper that considers interfering factors from past health records while detecting patients with cardiac problems. This proposed model is compared with two benchmark classifiers known as Support Vector Machine (SVM) and K-Nearest Neighbour (K-NN). The comparative analysis concludes that the proposed model offers enhanced efficiency for heart disease prediction. A promising result is given by the proposed method with an accuracy of 84.37%, F1-Score of 0.84 and MSE of 0.16.
Chung J, Gulcehre C, Cho K, Bengio Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. 2014;1–9.
Evgeniou T, Pontil M. Workshop on support vector machines: Theory and applications. Mach. Learn. Its Appl. Adv. Lect. 2001;249–257.
Cunningham P, Delany SJ. K-Nearest neighbour classifiers. Mult. Classif. Syst. 2007;1–17.
Chauhan A, Jain A, Sharma P, Deep V. Heart disease prediction using evolutionary rule learning. Int. Conf. Computational Intell. Commun. Technol. CICT. Cict. 2018; 1–4.
Gonsalves AH, Thabtah F, Mohammad RMA, Singh G. Prediction of coronary heart disease using machine learning: An experimental analysis. ACM Int. Conf. Proceeding Ser. 2019;51–56.
Austin PC, Tu JV, Ho JE, Levy D, Lee DS. Using methods from the data-mining and machine-learning literature for disease classification and prediction: A case study examining classification of heart failure subtypes. J. Clin. Epidemiol. 2013;66(4): 398–407.
Kirmani M. Cardiovascular disease prediction using data mining techniques. Orient. J. Comput. Sci. Technol. 2017; 10(2):520–528.
Sai PP, Reddy C. Heart disease prediction using ann algorithm in data mining. International Journal of Computer Science and Mobile Computing. 2017;6(4):168-172.
Bahrami B, Hosseini Shirvani M. Prediction and diagnosis of heart disease by data mining techniques. J. Multidiscip. Eng. Sci. Technol. 2015;2(2):3159–40.
Mohan S, Thirumalai C, Srivastava G. Effective heart disease prediction using hybrid machine learning techniques. IEEE Access. 2019;7:81542–81554.
Izci E, Ozdemir MA, Degirmenci M, Akan A. Cardiac arrhythmia detection from 2d ecg images by using deep learning technique. TIPTEKNO 2019 - Tip Teknol. Kongresi. 2019;1–4.
Blake C, Keogh E, Merz CJ. UCI repository of machine learning databases. Department of Information and Computer Science, University of California, Irvine, CA; 1998.
Lipton ZC, Berkowitz J, Elkan C. A critical review of recurrent neural networks for sequence learning. 2015;1–38.
Nwankpa C, Ijomah W, Gachagan A, Marshall S. Activation functions: Comparison of trends in practice and research for deep learning. 2018;1–20.
Canbek G, Temizel TT, Sagiroglu S, Baykal N. Binary classification performance measures/metrics: A comprehensive visualized roadmap to gain new insights,” 2nd Int. Conf. Comput. Sci. Eng. UBMK. 2017;821–826.
HM, SMN. a review on evaluation metrics for data classification evaluations. Int. J. Data Min. Knowl. Manag. Process. 2015;5(2):01–11.