Machine Learning Based Heart Disease Prediction via Multi-Dataset Harmonization and Structural Validation

This study addresses the critical limitations of dataset fragmentation and poor generalizability in cardiovascular machine learning by harmonizing three disparate public heart disease datasets into a unified 2,271-record benchmark with a standardized 14-feature schema. Rather than relying solely on predictive metrics, this work pioneers a post-integration structural validation approach using Pearson correlation analysis to statistically prove that the merged dataset maintains clinical coherence and reduces source-dependent feature bias. Furthermore, rigorous cross-dataset benchmarking demonstrates that models trained on this harmonized dataset—led by a Random Forest classifier achieving 85.27% accuracy and a 0.910 ROC-AUC—empirically outperform identical models trained on isolated baselines. Finally, the framework ensures medical transparency and clinical plausibility by augmenting traditional ensemble methods with a complementary Graph Neural Network (GNN) extension and SHAP-based explainability to map decisions directly to key physiological predictors.