A Review of Machine Learning Algorithms and Feature Selection Techniques for Cardiovascular Disease Prediction: Insights and Implications

Cardiovascular disease (CVD) is a formidable public health challenge across the globe and is the most prevalent cause of mortality. Early detection and accurate prediction of CVD can help prevent disease progression and reduce the risk of complications. Machine learning (ML) techniques show promising results in improving the accuracy and efficiency of CVD prediction to precision. However, the effectiveness of machine learning algorithms in CVD prediction largely depends on the selection of relevant features from complex datasets. The performance and interpretability of ML models are improved by feature selection strategies, which attempt to identify significant attributes while eliminating duplicate or irrelevant features. The feature selection and ML algorithms for CVD are thoroughly reviewed in this publication. The review provides insight into the selection of appropriate feature selection techniques and machine learning algorithms for accurate CVD prediction and evaluates the effectiveness and performance of these methods on cardiovascular datasets. Insights from the findings of this study can be used for interpreting the selection of optimal feature selection methods and ML algorithms for the precise prediction of cardiovascular disease, thereby improving patient outcomes and reducing healthcare costs.