Enhancing Coronary Heart Disease Risk Assessment with Machine Learning

This study uses machine learning approaches to improve the evaluation of risk for coronary heart disease (CHD). The study makes use of secondary data gathering, a design with descriptive characteristics, a deductive methodology, and an interpretive perspective as its foundation. A wide range of datasets including clinical, lifestyle, including socioeconomic information are used to train the models. With an AUC-ROC of 0.89, the results show better discriminative capacity than conventional risk models. Results of 0.88 as well as 0.82 for particularity and sensitivity, respectively, demonstrate how well the algorithms identify those who are at risk. Durability testing as well as sensitivity analysis highlight the models' resilience. Their applicability to a variety of demographics is confirmed by external validation. Future research should concentrate on integrating improved imaging as well as genetic analysis with contemporaneous patient information for dynamic risk analysis. It's also advised to investigate economic effect analysis and group learning strategies