Computational Intelligence Approaches for Heart Disease Prediction: A Comparative Evaluation

Heart disease is a worldwide health concern for which precise risk assessment and early detection need a call for solutions that are creative as well as accurate. Cardiovascular research has undergone a significant revolution because of advancements in computational intelligence (CI) techniques like machine learning (ML), which has improved diagnostic accuracy and identified new risk factors. To predict the risk of heart disease in the early stages, ML algorithms evaluate large chunks of diversified patient data, while also considering their lifestyle, genetic markers, and medical history. Some of the meticulous features for careful engineering and selecting methods required to create effective ML models include feature extraction, dimensionality reduction, hyperparameterization, etc. The decision support systems often provide pragmatic insights suitable to individualized treatment suggestions. These features of ML-based heart disease prediction are a beacon to bridge the gap between these predictions and actual clinical practices. Therefore, it would be suitable to conclude that ML has great potential to address patient-specific therapies, the early diagnosis of the disease, and the risk assessment in the context of heart diseases. This paper compares the performance of various CI approaches in heart disease prediction. It evaluates the performance of different evaluation metrics by varying the train test splits. It will help the researchers working in the relevant domain to select the most suitable model for designing the heart disease diagnostic system.