An Improved Coronary Heart Disease Predictive System Using Random Forest

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dc.contributor.authorAbdulraheem Abdul
dc.contributor.authorIsiaka, Mope Rafiu
dc.contributor.authorBabatunde, Ronke Seyi
dc.contributor.authorAjao, Jumoke Falilat
dc.date.accessioned2026-05-16T19:56:40Z
dc.date.available2026-05-16T19:56:40Z
dc.date.issued0021-08-11
dc.description.abstractAims: 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.
dc.identifier.urihttps://kwasuspace.kwasu.edu.ng/handle/123456789/7270
dc.language.isoen
dc.publisherAsian Journal of Research in Computer Science
dc.titleAn Improved Coronary Heart Disease Predictive System Using Random Forest
dc.typeArticle
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