Enhancing Cardiovascular Disease Prediction: Optimised Feature Selection and Machine Learning Techniques for Improved Accuracy

Cardiovascular disorders (CVD) make a notable contribution to the global death toll, signifying the urgent need for precise prediction and proactive management tools. This study investigates the incorporation of advanced feature selection techniques with machine learning models to provide better predictions regarding cardiovascular disease in terms of accuracy and clarity. A merged healthcare dataset was used to address the common challenges such as small data size, incomplete or missing data and high dimensionality. Feature selection and dimensionality reduction through PCA and SHAP were used according to how much variance and importance they applied to features. The result demonstrates that SHAP-extracted features, which are fewer, obtained better performance compared to PCA and full-feature models. In addition, the fewer features from SHAP offered a more computationally efficient and interpretable solution. These results underscore the potential of incorporating explainable AI into clinical decision-making processes and the early diagnosis of cardiovascular disease.