Improving Cardiovascular Prediction Performance Using Machine Learning Based Feature Selection

To date, cardiovascular disease (CVD) is responsible for a considerable number of deaths each year. Hence, developing an effective CVD prediction model is essential to reducing mortality rates. To this end, this paper makes use of different machine learning (ML) classifiers such as logistic regression (LG), K-nearest neighbor (KNN), support vector machine (SVM), gradient boosting (GB), and adaptive boosting (AdaB) to improve CVD prediction performance. In addition, this paper investigates the use of ML based on the ANOVA feature selection method and voting ensemble model by aggregating different ML classifiers to improve CVD prediction. The CVD performance is evaluated using key metrics such as accuracy, precision, recall, F1-score, and confusion matrix. The results demonstrate that the proposed approach achieves the highest CVD prediction accuracy compared to state-of-the-art methods recording 93.44%. The findings obtained in this paper suggest that the SVM ANOVA feature selection and ensemble approaches can be considered practical strategies for improving the prediction accuracy of CVD.