Machine Learning Models in Type 2 Diabetes Risk Prediction:Results from a Cross-sectional Retrospective Study in Chinese Adults

作     者：Xiao-lu XIONG Rong-xin ZHANG Yan BI Wei-hong ZHOU Yun YU Da-long ZHU 

作者机构：Department of EndocrinologyNanjing Drum Tower Hospital Clinical College of Nanjing Medical UniversityNanjing 210008China School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjing 211166China 

出 版 物：《Current Medical Science》 (当代医学科学（英文）)

年 卷 期：2019年第39卷第4期

页      面：582-588页

核心收录：

学科分类：10[医学] 

基　　金：This work was supported by grants from the National Natural Science Foundation of China (No.81570737, No.81370947, No.81570736, No.81770819, No.81500612, No.81400832, No.81600637, No.81600632, and No.81703294) the National Key Research and Development Program of China (No.2016YFC1304804 and No.2017YFC1309605) the Jiangsu Provincial Key Medical Discipline (No.ZDXKB2016012) the Key Project of Nanjing Clinical Medical Science the Key Research and Development Program of Jiangsu Province of China (No.BE2015604 and No.BE2016606) the Jiangsu Provincial Medical Talent (No.ZDRCA2016062) the Nanjing Science and Technology Development Project (No.201605019) 

主　　题：type 2 diabetes risk prediction machine learning 

摘      要：Type 2 diabetes mellitus (T2DM) has become a prevalent health problem in China,especially in urban *** prevention strategies are needed to reduce the associated mortality and *** applied the combination of rules and different machine learning techniques to assess the risk of development of T2DM in an urban Chinese adult population.A retrospective analysis was performed on 8000 people with non-diabetes and 3845 people with T2DM in *** Perceptron (MLP),AdaBoost (AD),Trees Random Forest (TRF),Support Vector Machine (SVM),and Gradient Tree Boosting (GTB) machine learning techniques with 10 cross validation methods were used with the proposed model for the prediction of the risk of development of *** performance of these models was evaluated with accuracy,precision,sensitivity,specificity,and area under receiver operating characteristic (ROC) curve (AUC).After comparison,the prediction accuracy of the different five machine models was 0.87,0.86,0.86,0.86 and 0.86 *** combination model using the same voting weight of each component was built on T2DM,which was performed better than individual *** findings indicate that,combining machine learning models could provide an accurate assessment model for T2DM risk prediction.