A Comprehensive Study of Machine Learning and Deep Learning for Heart Disease Prediction

Abstract

Cardiovascular diseases are the main reasons for death around the world at present, so early detection and intervention can be difficult. Review of Recent Applications of Machine Learning and Deep Learning in Cardiovascular Disease Prediction. Logistic regression, decision trees, random forests, support vector machines and gradient boosting have all been applied to the Cleveland and Kaggle cardiovascular datasets in previous studies. Based on research results, soft voting and stacking ensemble methods have been used to improve the prediction accuracy of a single classifier. Dense neural networks and hybrid Convolutional Neural Network - Long Short-Term Memory (CNN-LSTM) architectures are also deep learning models that have been researched and applied. Add to the above that SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) are popular methods for explaining Artificial Intelligence (AI) models. However, there are still many problems, such as an abundance of small public datasets, class imbalance, a lack of external validation, and poor clinical interpretability. The above are the problems of this paper, and some future directions for constructing a reliable cardiac disease prediction system with clinical applications are proposed.