Personalized Cardiovascular Risk Reduction: A Hybrid Recommendation Approach Using Generative Adversarial Networks and Machine Learning

Cardiovascular disease (CVD) is a leading cause of death worldwide and hence requires early risk assessment and focused preventative measures. The study describes a novel two-phase hybrid approach that combines machine learning-based CVD risk prediction and personalized lifestyle advice. In the first phase, cardiovascular risk is estimated using ensemble classifier that combines Random Forest Classifier, SVM and LR using metal learner trained on the Heart Disease dataset (1000 record, 14 attributes) has excellent predictive accuracy. In the second phase, optimization framework produces lifestyle suggestions that are safe for health within clinically determined parameters, which are enhanced using a hybrid recommendation system that combines content-based and Cluster-based Outcome Analysis. The suggested approach considerably outperformed a baseline of general lifestyle recommendations in a simulated high-risk cohort, exhibiting an average relative risk reduction of [X] % over a 10-year period as determined by the Framingham Risk Score. The suggested approach is made to be validated in future research using external datasets, simulated patient trials, and physician evaluation in order to guarantee clinical relevance This methodology highlights the promise for precision cardiovascular prevention by providing personalized, data-driven lifestyle recommendations.