HEART FAILURE DETECTION USING OPTIMIZATION ALGORITHMS

Heart failure (HF) remains a significant global health challenge, requiring early and precise detection to improve clinical outcomes and reduce mortality rates. Traditional diagnostic approaches often fail to capture the complexity of HF pathophysiology, necessitating advanced computational methods for accurate prediction. In this study, we propose a novel optimized Stacked Support Vector Machine (S-SVM) frame- work, integrating multiple SVM classifiers with diverse kernel functions to enhance predictive accuracy. A genetic algorithm (GA) is employed to fine-tune hyperparameters, ensuring model robustness and general- izability across patient populations. The model is rigorously evaluated on the UCI Heart Failure Clinical Records Dataset and the Framingham Heart Study Dataset, demonstrating superior performance in accuracy (95.7%), precision (0.90), recall (0.87), and AUC (0.96) compared to conventional machine learning tech- niques. The proposed system effectively balances computational efficiency with clinical interpretability, making it a promising tool for early-stage HF detection and risk stratification. This research advances the intersection of machine learning and cardiovascular diagnostics, offering a scalable and adaptive solution for real-world healthcare applications.