Advanced Machine Learning Techniques for Predicting Heart Disease: A Comparative Analysis Using the Cleveland Heart Disease Dataset

Abstract

The ability to predict heart illness was essential for prompt diagnosis and treatment. Using the Cleveland Heart Disease dataset, this study tested a number of machine learning models, including LSTM networks, Random Forest, Gradient Boosting, XGBoost, and Logistic Regression. In order to handle missing values, transform categorical variables, and binarize the target variable, the dataset underwent pre-processing. AUC-ROC, F1-score, recall, accuracy, and precision were used to assess each model. SHAP values shed light on the significance of each characteristic. The results showed that XGBoost was the most accurate model, exceeding the other models with an accuracy of 90% and an AUC-ROC of 0.94. This study highlighted the potential of advanced machine learning techniques for improving heart disease prediction and contributed to the development of better diagnostic tools for patient care.