文章基本信息

标题：Prediction of Type 2 Diabetes Risk and Its Effect Evaluation Based on the XGBoost Model
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作者：Liyang Wang ; Xiaoya Wang ; Angxuan Chen 等
期刊名称：Healthcare
电子版ISSN：2227-9032
出版年度：2020
卷号：8
期号：3
页码：247-257
DOI：10.3390/healthcare8030247
出版社：MDPI Publishing
摘要：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.
关键词：ensemble learning; prediction of disease risk; XGBoost model; traditional machine learning; comparative analysis ensemble learning ; prediction of disease risk ; XGBoost model ; traditional machine learning ; comparative analysis