Machine learning-based assessment of diabetes risk

Currently, diabetes is one of the most dangerous diseases in modern society. Prevention is an extremely important aspect in the field of medicine, and the field of artificial intelligence and the healthcare industry are penetrating and integrating with each other, and combining machine models for prediction and diagnosis of diabetes is a big trend. In order to validate the advantages and potential of XGBoost model in the field of diabetes prediction, this study identified 10 key features by processing a medical examination dataset containing 556,495 sample size. Among them, glycated hemoglobin has high clinical value as a predictor. By constructing six machine models (XGBoost, Decision Tree, Logistic Regression, Random Forest, CatBoost, and LightGBM) and comparing their performances, we finally obtained that: the performance of XGBoost is relatively the best, with accuracy of 97.5%, recall of 97%, F1 score of 96.9%, and ROC-AUC score of 0.971.