HEART DISEASE CLASSIFICATION USING XGBOOST CLASSIFIER

Heart disease remains one of the leading causes of mortality worldwide, creating an urgent need for accurate and early diagnostic systems that can support healthcare professionals in clinical decision-making. Traditional diagnostic procedures often rely on manual interpretation of clinical parameters such as electrocardiogram readings, cholesterol levels, blood pressure, and patient medical history. While these approaches are effective in many cases, they may fail to capture complex nonlinear relationships among multiple risk factors and may also be affected by time constraints and subjective interpretation. Recent advancements in machine learning provide promising opportunities for developing automated and intelligent diagnostic systems capable of analyzing large volumes of healthcare data with improved accuracy and efficiency. This research proposes a heart disease classification system using the Extreme Gradient Boosting (XGBoost) algorithm to predict the presence of cardiovascular disease based on clinical attributes. The proposed model incorporates several stages including data preprocessing, feature engineering, model training, and performance evaluation. The system utilizes structured medical datasets containing various patient attributes such as age, gender, chest pain type, cholesterol level, blood pressure, fasting blood sugar, and electrocardiographic results. XGBoost is selected due to its ability to handle missing data, manage nonlinear relationships, and optimize predictive performance through gradient boosting techniques. Experimental evaluation demonstrates that the proposed system achieves high predictive accuracy while maintaining strong precision, recall, and F1-score values. The model can assist clinicians by providing early risk prediction and supporting informed medical decisions. Overall, the proposed approach contributes to the advancement of AI-based healthcare systems by enabling efficient, scalable, and data-driven heart disease prediction mechanisms.