AI- Based Heart Disease Detection Using Machine Learning

Abstract

Heart disease continues to be among the top causes of death globally, and early and precise diagnosis is required for proper treatment. Advances in artificial intelligence (AI) and machine learning (ML) in recent times have made it possible to create predictive models that can aid in early detection and risk prediction of heart disease. This work suggests a machine learning method of heart disease detection from patient health information, with clinical factors such as blood pressure, cholesterol levels, and lifestyle. A range of ML algorithms, from logistic regression and decision trees to support vector machines and deep models, were trained and tested for predictive performance. Feature selection methods were used to optimize model performance and interpretability. Experimental findings show that AI-based models are capable of high accuracy, sensitivity, and specificity in identifying heart disease with better performance than conventional diagnostic strategies. The paper identifies the usefulness of AI-driven decision support systems in medicine for assisting clinicians with early diagnosis and enhancing patient care. Future directions will include real-time deployment, model interpretability, and coupling with electronic health records for deployment in clinical practice.