An Improved Coronary Heart Disease Predictive System Using Random Forest
Abdulraheem Abdul *
Department of Computer Science, Kwara State University, Malete, Nigeria.
Rafiu M. Isiaka
Department of Computer Science, Kwara State University, Malete, Nigeria.
Ronke S. Babatunde
Department of Computer Science, Kwara State University, Malete, Nigeria.
Jumoke F. Ajao
Department of Computer Science, Kwara State University, Malete, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
Aims: This work aim is to develop an enhanced predictive system for Coronary Heart Disease (CHD).
Study Design: Synthetic Minority Oversampling Technique and Random Forest.
Methodology: The Framingham heart disease dataset was used, which was collected from a study in Framingham, Massachusetts, the data was cleaned, normalized, rebalanced. Classifiers such as random forest, artificial neural network, naïve bayes, logistic regression, k-nearest neighbor and support vector machine were used for classification.
Results: Random Forest outperformed other classifiers with an accuracy of 98%, a sensitivity of 99% and a precision of 95.8%. Feature selection was employed for better classification, but no significant improvement was recorded on the performance of the classifier with feature selection. Train test split also performed better that cross validation.
Conclusion: Random Forest is recommended for research in Coronary Heart Disease prediction domain.
Keywords: Coronary heart disease, machine learning, random forest, artificial neural network, K-nearest neighbor, support vector machine, and naïve bayes