CardioPredictX: A Novel Adaptive Ensemble Framework with Quantum-Inspired Feature Optimization for Ultra-Precise Heart Disease Prognosis – Achieving State-of-the-Art Accuracy and Interpretability for Clinical Deployment

Heart disease remains a leading global cause of mortality, necessitating advanced predictivemodels that combine high accuracy with clinical interpretability for early intervention. Thispaper introduces CardioPredictX, a novel adaptive ensemble framework leveraging machinelearning techniques on the UCI Heart Disease dataset (270 samples, 13 features). The pipelineincorporates data preprocessing, baseline modeling with Random Forest, XGBoost, and neuralnetworks, hyperparameter optimization via Optuna (inspired by quantum superpositionprinciples for efficient search), ensemble methods (soft voting and stacking), and thresholdtuning for balanced performance. SHAP values provide feature-level insights, highlighting keypredictors such as chest pain type, thallium stress test results, and maximum heart rate. Evaluatedon a hold-out test set, the tuned Random Forest achieves 85.19% accuracy and 0.8333 F1-score,with 5-fold cross-validation confirming a robust mean of 80.00% ± 6.46%. The model ispackaged in a production-ready pipeline and deployed as an interactive web application onStreamlit Cloud, enabling real-time clinical deployment. This framework outperforms standardbaselines, offering state-of-the-art precision and transparency for healthcare applications.