Predicting Coronary Artery Disease Risk with Metaheuristic-Enhanced Machine Learning Models
Keywords:
Machine Learning, Coronary Artery Disease, Prediction, Risk AnalysisAbstract
Traditional risk assessment techniques frequently rely on static, constrained information and do not adequately account for the dynamic nature of CAD development. The feature selection and model hyperparameters are optimised by our suggested framework, which makes use of the capabilities of metaheuristic algorithms like genetic algorithms and particle swarm optimisation. This dynamic method enhances forecast accuracy while also making it possible to spot important risk variables that could otherwise go unnoticed.Our studies make use of a large cohort of CAD patients with a variety of demographic, clinical, and genetic data. We contrast the performance of models augmented by metaheuristics with that of traditional machine learning techniques. The findings show a considerable increase in the accuracy of CAD risk prediction, with improved models routinely surpassing their conventional counterparts.Additionally, our method sheds light on unexpected correlations that can guide personalised prevention initiatives while also offering insightful information about the relative importance of distinct risk factors. We open the door for more focused therapies by finding hidden patterns in the data, thereby lessening the impact of CAD on healthcare systems and enhancing patient outcomes.Metaheuristic techniques are added to CAD risk prediction to improve accuracy as well as interpretability and generalizability. Our methodology has the potential to completely alter how we think about disease risk assessment and can be modified for other difficult medical problems. Ultimately, early CAD identification shows potential for the incorporation of metaheuristic-enhanced machine learning models into clinical practise, leading to more effective preventative and management measures.
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