Predictive Models for Heart Attack Disease Risk
Data mining techniques have been widely applied to the diagnosis of heart diseases. This paper develops models that predict the risk of an individual developing a heart attack. This is with a view to formulating a predictive model to determine the likelihood of having heart attack in a patient, simulating the model formulated, and evaluating the performance of the model. The predictive models were formulated and simulated using Rapid Miner Studio - a collection of knowledge discovery and machine learning tools. The models were developed using Decision-tree, Naïve-Bayes, and Bagging classifiers. The inputs for these prediction models were risk factors of heart attack. A hybrid feature selection technique was carried out and the features selected were used as input variables to the classifiers. 10-fold cross validation was used to assess the performance of the algorithms in predicting a class. The models’ performance was evaluated using accuracy and sensitivity as metrics. The Naïve Bayes model exhibits a better performance with an accuracy of 87.86%. The model is expected to enhance the decision-making process of cardiologist.
Agarwal V., Briasoulis A. and Messerli F. H. (2013). Effects of renin-angiotensin system blockade on mortality and hospitalization in heart failure with preserved ejection fraction. Heart Failure Reviews, Vol. 4, Issue 3, pp. 429-437.
Amin S. U., Agarwal K. and Beg R. (2013). Genetic Neural Network Based Data Mining in Prediction of Heart Disease Using Risk Factors. Proceedings of 2013 IEEE Conference on Information and Communication Technologies (ICT 2013), Vol. 1, Issue 1 pp. 1228-1231.
Chaurasia V. and Pal S. (2013). Early Prediction of Heart Diseases Using Data Mining Techniques,” Caribbean Journal of Science and Technology, Vol. 1, pp. 208-217.
Dangare C. S. and Apte S. S. (2012). A Data Mining Approach for Prediction of Heart Disease using Neural Networks. International Journal of Computer Engineering & Technology, Vol. 3, Issue 3, pp. 20-30.
Florence S., Bhuvaneswari N. G., Amma C., Annapoorani G. and Malathi K. (2014). Predicting the Risk of Heart Attacks using Neural Network and Decision Tree. International Journal of Innovative Research in Computer and Communication Engineering, Vol. 2, Issue11, pp. 7025-7030.
Hall, M. A. (1999). Correlation-based feature selection for machine learning. Unpublished Ph.D thesis, Department of Computer Science, University of Waikato, Hamilton, New Zealand.
Joshi S. and Nair M. K. (2015). Prediction of Heart Disease Using Classification Based Data Mining Techniques. Computational Intelligence in Data Mining, Vol. 2 Issue 1, pp. 503-511.
Mackay J. and Mensah G. A. (2004). The Atlas of Heart Disease and Stroke. Cardiovascular Disease, World Health Organization. [Online] Available at: https://www.who.int/cardiovascular_diseases/resources/atlas/en/ Accessed: 25th July, 2020.
Masethe H. D. and Masethe M. A. (2014). Prediction of Heart Disease using Classification Algorithms. Proceedings of the World Congress on Engineering and Computer Science, Vol. 2, Issue 1, pp. 25-29.
Mendis S., Abegunde D., Yusuf S., Ebrahim S., Shaper G., Ghannem H. and Shengelia B. (2005). “WHO study on Prevention of Recurrences of Myocardial Infarction and Stroke (WHO-PREMISE),” Bulletin of the World Health Organization, Vol. 83, Issue 3, pp. 820-829.
Nikhar S. and Karandikar A. M. (2016). Prediction of Heart Disease Using Machine Learning Algorithms. International Journal of Advanced Engineering, Management and Science (IJAEMS), Vol. 2, Issue 6, pp. 617-621.
Roger V. L., Go A. S., Lloyd-Jones D. M., Benjamin E. J., Berry J. D., Borden W. B. and Fullerton H. J. (2012). Heart disease and stroke statistics - 2012 update. A report from the American Heart Association, Circulation, Vol. 125, Issue 1, pp. 2-220.
Soni J., Ansari U. and Sharma D. (2011). Intelligent and Effective Heart Disease Prediction System using Weighted Associative Classifiers. International Journal on Computer Science and Engineering, Vol. 3 Issue 6, pp. 43 – 48.
Taneja T., Gaurav G. and Sethi A. (2014). Study of classifiers in Data Mining. International Journal of Computer Science and Mobile Computing, Vol. 3, Issue 9, pp. 263-269.
Thuraisingham B. (2000). A primer for understanding and applying data mining. Institute of Electrical Electronics Engineers, IT Professional, Vol. 2, Issue 1, pp. 28-31.
WHO (2007). A Safer Future: Global Public Health Security in the 21st Century. The World Health Report. [Online] Available at: https://www.who.int/whr/2007/whr07_en.pdf. Accessed: 25th July, 2020.
WHO (2009). Commission on Macroeconomics and Health. Improving health outcomes of the poor. Report of working group 5 Geneva, Switzerland. [Online] Available at: http://who.int/iris/bitstream/10665/42488/1/9241590130.pdf. Accessed: 25th July, 2020.
Yildirim P. (2015). Filter-based feature selection methods for prediction of risks in hepatitis disease. International Journal of Machine Learning and Computing, Vol. 5 Issue 4, pp. 258.