Introduction: Heart disease is known as one of the most important causes of death in today's society and so far no definitive method has been found to predict it and several factors are effective in contracting this disease. Therefore, the aim of this study was to provide a data mining model for predicting heart disease.
Material and Methods: This study used standard data from UCI. These data include four Cleveland, Hungarian, Swiss and Long Beach VA databases. These data include 13 independent variables and one dependent variable. The data are missing, and the EM algorithm was used to control this loss, and at the end of the data, a suggestion algorithm was implemented that combined the two random forest algorithms and the artificial neural network.
Results: In this study, data was divided into two training sets and 10-Fold method was used. To evaluate the algorithms, three indicators of sensitivity, specificity, accuracy were used and the accuracy of the prediction algorithm for four data Cleveland, Hungarian, Switzerland and Long Beach VA reached 87.65%, 94.37%, 93.45% and 85%, respectively. Then, the proposed algorithm was compared with similar articles in this field, and it was found that this algorithm is more accurate than similar methods.
Conclusion: The results of this study showed that by combining the two algorithms of random forest and artificial neural network, a suitable model for predicting heart attacks can be provided.
R. DeSilva, Heart Disease. ABC-CLIO, 2013, p. 306.
E. J. Benjamin et al., "Heart Disease and Stroke Statistics'2017 Update: A Report from the American Heart Association," (in English (US)), Circulation, vol. 135, no. 10, pp. e146-e603, 2017/03/07 2017.
S. R. Gregson, Heart Disease. Capstone, 2001, p. 72.
R. Jothikumar, S. Susi, N. Sivakumar, and P. S. Ramesh, "Predicting life time of heart attack patient using improved c4.5 classification algorithm," Research Journal of Pharmacy and Technology, vol. 11, no. 5, pp. 1951-1956, 2018 2018.
S. Prakash, K. Sangeetha, and N. Ramkumar, "An optimal criterion feature selection method for prediction and effective analysis of heart disease," ed, 2018, p. 1.
T. Puyalnithi and M. V. Vankadara, "Prediction of transition sequence of diseases’ severity levels using clinical datasets with data mining approaches," Biomedical Research (India), vol. 28, no. 15, pp. 6900-6906, 2017 2017.
W.-J. Lu, M. Liang, and C. Hao, "Particle Swarm Optimisation-Support Vector Machine Optimised by Association Rules for Detecting Factors Inducing Heart Diseases," Journal of Intelligent Systems, vol. 26, no. 3, pp. 573-583, 2017 2017.
M. Yahyaie, M. J. Tarokh, and M. A. Mahmoodyar, "Use of Internet of Things to Provide a New Model for Remote Heart Attack Prediction," (in eng), Telemedicine journal and e-health : the official journal of the American Telemedicine Association, vol. 25, no. 6, pp. 499-510, Jun 2019.
E. J. Benjamin et al., "Heart Disease and Stroke Statistics-2019 Update: A Report From the American Heart Association," (in eng), Circulation, vol. 139, no. 10, pp. e56-e528, Mar 5 2019.