Impact of feature selection and feature engineering in prediction of cardiovascular diseases

Heart disease remains a leading cause of death worldwide, presenting major challenges to public health. As a complex cardiovascular disorder (CVD), it often leads to life threatening complications such as heart attacks, strokes, and heart failure. Early and accurate diagnosis is essential for reducing fatality rates and ensuring better clinical results. While ML models have shown great potential in identifying cardiovascular diseases but their effectiveness heavily depends on optimal feature selection and engineering. This study presents a novel approach integrating feature selection and feature engineering techniques to enhance heart disease prediction using ML classifiers. Firstly, four key attributes were selected from a combined heart disease prediction dataset using RF model. Subsequently, a feature engineering technique have been applied to generate thirty six new features through basic arithmetic operations, strengthening dataset to improve predictive efficiency. These newly generated features have been utilized to train ML classifiers. To further enhance the classification accuracy, an ensemble learning approach based on soft voting have been applied to mitigate the impact of weaker classifiers. The effectiveness of the proposed methodology have been evaluated by comparing model performance with and without feature selection and engineering. The RF model achieved superior classification results, with accuracy, precision, recall, F1-score, AUC-ROC, and Jaccard score reaching 96.56%, 97.83%, 95.26%, 96.53%, 99.55%, 93.29% for RF model, respectively. Similarly, with feature engineering technique, the DT model attained 95.23%, 94.32%, 96.31%, 95.31%, 96.14%, 91.04%, respectively. Notably, RF and DT models demonstrated superior performance incorporating feature selection and engineering techniques. The present study shows high disease prediction accuracy as compared to various feature selection techniques and also, shows the significance of feature engineering in enhancing ML models, enabling medical professionals to diagnose diseases more effectively and at earlier stages.