Abstract

: Conventional risk assessment methods often depend on fixed, limited data and fail to sufficiently consider the ever-changing nature of CAD development. Our recommended approach utilizes metaheuristic techniques such as genetic algorithms and particle swarm optimization to optimize the feature selection and model hyperparameters. By using this dynamic approach, the accuracy of forecasts is improved, and it also enables the identification of significant risk factors that could otherwise be overlooked. We use a substantial cohort of individuals diagnosed with coronary artery disease (CAD), including diverse demographic, clinical, and genetic information. We compare the effectiveness of models enhanced by metaheuristics with that of conventional machine learning approaches. The results demonstrate a significant improvement in the precision of CAD risk prediction, as the upgraded models consistently outperform their traditional counterparts. Furthermore, our approach illuminates unforeseen connections that might influence tailored preventative efforts, while also providing valuable insights into the comparative significance of different risk variables. By uncovering concealed trends in the data, we facilitate the development of targeted medicines, reducing the burden of CAD on healthcare systems and improving patient outcomes. Metaheuristic approaches are included into CAD risk prediction to enhance both the accuracy and the interpretability and generalizability of the results. The promise of our technique is to fundamentally transform our understanding of illness risk assessment and may be used for other complex medical challenges. In conclusion, the early detection of CAD has the potential to integrate metaheuristic-enhanced machine learning models into clinical practice, resulting in more efficient preventive and therapeutic strategies.Keywords: Machine Learning, Coronary Artery Disease, Prediction, Risk Analysi