Recommendation System for Stroke Risk Prediction using Machine Learning Approaches

Early detection and prevention of strokes are crucial for lowering morbidity and death. To evaluate stroke risk, this study uses predictive modeling, which includes clinical biomarkers, lifestyle factors, and demographic data. Machine learning techniques including logistic regression, gradient boosting and random forest are used to a variety of datasets. The result is a personalized risk score, which encourages high-risk patients to seek medical attention. Individual risk profiles are used to develop customized interventions such as medication, lifestyle changes, and exercise suggestions. This proactive method promises to reduce stroke incidence while improving health outcomes, potentially transforming early detection and preventive strategies.