Advanced Machine Learning for Heart Disease Prediction A Comparative Study of Ensemble and Traditional
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Date
2025-06-30
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University of Msila
Abstract
Heart disease, a leading global cause of mortality, demands innovative early detection
strategies. This study evaluates machine learning models (DNN, KNN, SVM, XGBoost,
Random Forest) for predicting cardiovascular disease using 1,000 patient records with 16
clinical features. After rigorous preprocessing and validation, ensemble methods like XGBoost
(100% accuracy) and Random Forest (99%) outperformed traditional models, highlighting
their clinical potential. Challenges such as overfitting and interpretability were addressed,
emphasizing the need for diverse datasets and explainable AI (XAI). Future integration with
wearable technologies and interdisciplinary collaboration could enable proactive, personalized
care, transforming cardiovascular health outcomes globally.