Summary
Type 2 diabetes mellitus (T2DM) has become a prevalent health problem in China, especially in urban areas. Early prevention strategies are needed to reduce the associated mortality and morbidity. We 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 Nanjing. Multilayer 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 T2DM. The 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 respectively. The combination model using the same voting weight of each component was built on T2DM, which was performed better than individual models. The findings indicate that, combining machine learning models could provide an accurate assessment model for T2DM risk prediction.
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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), and the Nanjing Science and Technology Development Project (No. 201605019).
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Xiong, Xl., Zhang, Rx., Bi, Y. et al. Machine Learning Models in Type 2 Diabetes Risk Prediction: Results from a Cross-sectional Retrospective Study in Chinese Adults. CURR MED SCI 39, 582–588 (2019). https://doi.org/10.1007/s11596-019-2077-4
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DOI: https://doi.org/10.1007/s11596-019-2077-4