Cardiovascular Disease Prediction and Prevention: Exploring Novel Techniques and Applications Using Machine Learning

Heart disease, stroke, and other cardiovascular conditions are a major cause of death and disability worldwide. The negative effects of CVDs must be lessened through early prediction and efficient prevention techniques. In this research, a novel AI-driven system is developed to improve CVD prediction accuracy and timeliness and to enable proactive management. The healthcare data set is analysed by the framework using machine learning methods including SVM, KNN, and Random Forest. Next, real-world clinical data is used to evaluate the framework's predictive capabilities, proving its capacity to render reliable risk evaluations. Additionally, the framework provides proactive management suggestions for individual patients, going above and beyond simple prediction. The findings highlight the promise of machine learning to significantly improve cardiovascular disease management. Our paradigm provides a viable path towards better patient outcomes and a lighter societal cost from CVDs by making accurate predictions early on and providing insights into how to put those predictions into action. This research lays the groundwork for the future of CVD prevention and management in the era of AI and individualised therapy.