Advanced Machine Learning for Heart Disease Prediction A Comparative Study of Ensemble and Traditional
| dc.contributor.author | BELAADA Wahid Akram | |
| dc.contributor.author | AFAN Mohamed | |
| dc.date.accessioned | 2025-07-20T10:24:35Z | |
| dc.date.available | 2025-07-20T10:24:35Z | |
| dc.date.issued | 2025-06-30 | |
| dc.description.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. | |
| dc.identifier.other | EL/029/25 | |
| dc.identifier.uri | https://repository.univ-msila.dz/handle/123456789/47375 | |
| dc.language.iso | en | |
| dc.publisher | University of Msila | |
| dc.title | Advanced Machine Learning for Heart Disease Prediction A Comparative Study of Ensemble and Traditional | |
| dc.type | Thesis |