Survey of Heart Disease Prediction Models using Machine Learning and Deep Learning Techniques

heart disease remains the leading cause of mortality worldwide, accounting for millions of deaths annually. Despite medical advancements, the early detection and accurate prediction of cardiovascular diseases (CVDs) remain critical challenges. This study explores the application of Machine Learning (ML) and Deep Learning (DL) techniques in predicting heart disease using clinical data, medical images, and physiological signals. Traditional ML models, including Decision Trees, Support Vector Machines (SVM), Random Forest, and Gradient Boosting (XGBoost, LightGBM), are evaluated alongside advanced DL models such as Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). A hybrid CNN + LSTM model is proposed to enhance predictive accuracy by leveraging both spatial and sequential data analysis. The research methodology involves data preprocessing, feature selection, model implementation, and comparative evaluation using performance metrics such as Accuracy, Precision, Recall, F1-Score, and AUC-ROC. Experimental results indicate that deep learning models, particularly CNN and hybrid models, outperform traditional ML approaches in heart disease prediction. Further research is needed to improve interpretability and real-time deployment of AI-driven approaches in clinical settings to improve early cardiovascular disease detection and risk assessment.