The XGBoost model predicted the risk of type 2 diabetes with an average accuracy of 0.8909 and an AUC of 0.9182, outperforming SVM, Random Forest, and K-NN algorithms.
Cross-Sectional (n=380)
No
Does the XGBoost model improve the prediction of type 2 diabetes risk compared to SVM, RF, and K-NN in middle-aged and elderly people?
The XGBoost model demonstrates superior accuracy and generalization ability in predicting type 2 diabetes risk compared to traditional machine learning algorithms.
Effect estimate: Accuracy 0.8909, AUC 0.9182
In view of the harm of diabetes to the population, we have introduced an ensemble learning algorithm-EXtreme Gradient Boosting (XGBoost) to predict the risk of type 2 diabetes and compared it with Support Vector Machines (SVM), the Random Forest (RF) and K-Nearest Neighbor (K-NN) algorithm in order to improve the prediction effect of existing models. The combination of convenient sampling and snowball sampling in Xicheng District, Beijing was used to conduct a questionnaire survey on the personal data, eating habits, exercise status and family medical history of 380 middle-aged and elderly people. Then, we trained the models and obtained the disease risk index for each sample with 10-fold cross-validation. Experiments were made to compare the commonly used machine learning algorithms mentioned above and we found that XGBoost had the best prediction effect, with an average accuracy of 0.8909 and the area under the receiver's working characteristic curve (AUC) was 0.9182. Therefore, due to the superiority of its architecture, XGBoost has more outstanding prediction accuracy and generalization ability than existing algorithms in predicting the risk of type 2 diabetes, which is conducive to the intelligent prevention and control of diabetes in the future.
Wang et al. (Fri,) conducted a cross-sectional in Type 2 diabetes (n=380). XGBoost model vs. Support Vector Machines, Random Forest, and K-Nearest Neighbor algorithms was evaluated on Prediction of type 2 diabetes risk (Accuracy 0.8909, AUC 0.9182). The XGBoost model predicted the risk of type 2 diabetes with an average accuracy of 0.8909 and an AUC of 0.9182, outperforming SVM, Random Forest, and K-NN algorithms.