Analysis and Study of Diabetes Follow-Up Data Using a Data-Mining-Based Approach in New Urban Area of Urumqi, Xinjiang, China, 2016-2017

Te focus of this study is the use of machine learning methods that combine feature selection and imbalanced process (SMOTE algorithm) to classify and predict diabetes follow-up control satisfaction data. Afer the feature selection and unbalanced process, diabetes follow-up data of the New Urban Area of Urumqi, Xinjiang, was used as input variables of support vector machine (SVM), decision tree, and integrated learning model (Adaboost and Bagging) for modeling and prediction. Te experimental results show that Adaboost algorithm produces better classifcation results. For the test set, the G-mean was 94.65%, the area under the ROC curve (AUC) was 0.9817, and the important variables in the classifcation process, fasting blood glucose, age, and BMI were given. Te performance of the decision tree model in the test set is relatively lower than that of the support vector machine and the ensemble learning model. Te prediction results of these classifcation models are sufcient. Compared with a single classifer, ensemble learning algorithms show diferent degrees of increase in classifcation accuracy. Te Adaboost algorithm can be used for the prediction of diabetes follow-up and control satisfaction data.