An Improved Genetic- XGBoost Classifier For Heart Disease Prediction

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 ۹۵%, 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.

کلیدواژه ها:

Genetic algorithm ، XGBoost Classifier ، Heart Disease Prediction