XGBoost for Heart Disease Prediction: Achieving High Accuracy with Robust Machine Learning Techniques
Keywords:
Heart Disease Prediction, Machine Learning, XGBoost, Predictive ModelingAbstract
Examining the efficacy of the XGBoost algorithm, this study investigates the use of machine learning for the prediction of cardiac illness. We tested XGBoost against Decision Trees and Random Forests using a Kaggle dataset that included more than 1,000 patient records with fourteen important features. With an F1-score of 0.9816, a recall of 1.0, a precision of 96.39%, and an accuracy of 98.04%, our hyperparameter-optimized XGBoost model performed admirably. Prior approaches, such the hybrid Random Forest model, which achieved an accuracy of 88.7 percent with a reduced dataset, are surpassed by this model. There were no missing instances and few false positives, demonstrating the XGBoost model's dependability in predicting heart disease. Its recall and accuracy were also good. Our results show that XGBoost might be a powerful tool for early detection of cardiac disease; it offers major gains over current methods and lays the groundwork for predictive analytics studies in the future.
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