Heart Disease Prediction using Machine Learning

– With the rise in cardiovascular diseases (CVDs), machine learning (ML) has become a key tool in early diagnosis using clinical data. Studies utilize algorithms like SVM, Random Forest, ANN, Decision Trees, and ensemble models, stressing the importance of data preprocessing and feature selection (e.g., PCA, RFE) for improved accuracy. Hybrid and ensemble techniques enhance predictive performance, while model interpretability remains vital for clinical use. Overall, ML models, when properly trained and tuned, significantly aid in the early detection of heart disease and support timely medical intervention.