An optimized XGBoost based diagnostic system for effective prediction of heart disease

Researchers have created several expert systems over the years to predict heart disease early and assist cardiologists to enhance the diagnosis process. We present a diagnostic system in this paper that utilizes an optimized XGBoost (Extreme Gradient Boosting) classifier to predict heart disease. Proper hyper-parameter tuning is essential for any classifier’s successful application. To optimize the hyper-parameters of XGBoost, we used Bayesian optimization, which is a very efficient method for hyper-parameter optimization. We also used One-Hot (OH) encoding technique to encode categorical features in the dataset to improve prediction accuracy. The efficacy of the proposed model is evaluated on Cleveland heart disease dataset and compared it with Random Forest (RF) and Extra Tree (ET) classifiers. Five different evaluation metrics: accuracy, sensitivity, specificity, F1-score, and AUC (area under the curve) of ROC charts were used for performance evaluation. The experimental results showed its validity and efficacy in the prediction of heart disease. In addition, proposed model displays better performance compared to the previously suggested models. Moreover, our proposed method reaches the high prediction accuracy of 91.8%. Our results indicate that the proposed method could be used reliably to predict heart disease in the clinic.