Prediction Model of Diabetes Based on Machine Learning

Diabetes mellitus is a metabolic disorder caused by the absolute insufficient secretion of insulin and the disorder of insulin utilization. Diabetes mellitus will bring great harm to the organs, and the complications of diabetes will pose a great threat to the health and life of patients, and even lead to disability and death. The prediction of diabetes has always been a hot topic, but it is very difficult to predict. From a medical point of view, in this study, we aim to establish a diabetes prediction model based on machine learning and data mining. We first proposed a dual characteristic variable selection method based on single-factor regression and LightGBM, which can screen out the medical indicators affecting diabetes. On this basis, we built a single diabetes prediction model based on machine learning, and further studied XGBoost and ResNet. Finally, we used $\text{GA}^{2}$Ms, XGBoost and ResNet to study the diabetes prediction model based on ensemble learning. The results show that the accuracy, F1 and AUC of the prediction model are 0.853, 0.888 and 0.875 respectively after five-fold cross-validation and comparative analysis, which are significantly better than other machine learning models. Therefore, the proposed method can accurately predict diabetes, so as to provide effective clinical auxiliary diagnosis for doctors, help doctors take preventive measures in advance, improve the survival rate of patients, and reduce the impact of diabetes on patients.