An integrated deep learning framework for heart disease prediction: combining DGCN feature extraction, fuzzy DBSCAN clustering, and optimized EA-DLSTM with SHAP interpretability

Heart disease, which includes conditions such as heart failure, coronary artery disease, and ventricular fibrillation, represents a major health challenge worldwide. The increasing mortality rate from cardiovascular disease (CVD) underscores the need for accurate and effective diagnostic methods. Although past studies have investigated machine learning (ML) and deep learning (DL) algorithms, issues like data imbalance, feature selection, and model optimization remain. This research introduces a comprehensive heart disease prediction system utilizing the Cleveland and UCI datasets. The methodology involves gathering and analyzing patient data, followed by data preparation using the Interquartile Range (IQR) and Synthetic Minority Oversampling Technique (SMOTE) to handle missing values. The Deep Graph Correlation Network (DGCN) is employed to extract distinct features from the pre-processed data. The Density-based Spatial Clustering of Applications with Noise (DBSCAN) method, enhanced with fuzzy logic, is used to segment the dataset. For predicting disease, the Adaptive Walrus Optimization Algorithm (WaOA) improves the Evolutionary Attention-based Deep Long Short-Term Memory (EA-DLSTM) model. This approach achieves accuracy rates of 99.62% for the UCI dataset and 99.71% for the Cleveland dataset, with precision scores of 99.75% and 99.68%, respectively. SHAP (SHapley Additive exPlanations) values ensure the model’s transparency and interpretability by emphasizing the contributions of key features to the predictions. This integrated strategy not only achieves high accuracy but also offers an interpretable and dependable system for the early detection of heart disease, effectively bridging the gap between automated predictions and clinical validation.