چکیده مقاله
Cardiovascular disease CVD remains one of the leading causes of mortality in developed societies, surpassing other fatal conditions in recent years Accurate prediction of heart disease poses significant challenges, as it requires a deep understanding of complex medical data and advanced analytical techniques In this study, we propose an innovative approach that combines the Genetic Algorithm GA and Gradient Boosting XGBoost to predict the likelihood of heart disease The Genetic Algorithm is utilized for optimizing the hyperparameters of the XGBoost model, ensuring enhanced performance and reliability The proposed hybrid method has been evaluated against various state of the art machine learning techniques to validate its efficacy Experimental results demonstrate that the proposed method achieves a superior accuracy of 95%, outperforming existing approaches This significant improvement underscores the potential of GA XGBoost as a robust tool for early and precise heart disease prediction, contributing to better clinical decision making and improved patient outcomes
کلیدواژهها
نویسندگان
Mahlagha Afrasiabi
Assistant professor, Department of Electrical and Computer Engineering, Hamedan University of Technology, Hamedan, Iran
Mahdi Rasoulinia
Undergraduate student, Department of Electrical and Computer Engineering, Hamedan University of Technology, Hamedan, Iran
شیوه ارجاع
Afrasiabi, Mahlagha and Rasoulinia, Mahdi,1403,An Improved Genetic- XGBoost Classifier For Heart Disease Prediction,3nd International Conference and 8th National Conference on Computers, information technology and applications of artificial intelligence,Ahvaz
ارائهشده در
مجموعه مقالات سومین کنفرانس بین المللی و هشتمین کنفرانس ملی کامپیوتر، فناوری اطلاعات و کاربردهای هوش مصنوعی30 بهمن 1403 · اهواز