An Improved Coronary Heart Disease Predictive System Using Random Forest
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Date
0021-08-11
Journal Title
Journal ISSN
Volume Title
Publisher
Asian Journal of Research in Computer Science
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.