Construction of Diabetes Prediction Model Based on Machine Learning

Diabetes is a global epidemic of chronic diseases; early identification of high-risk groups can effectively reduce the incidence of diabetes and reduce the risk of complications. In recent years, the predictive model based on machine learning has been gradually applied to the early screening and prediction of diabetes due to its efficient data processing ability. In this study, a diabetes prediction model was constructed by analyzing and comparing common machine learning algorithms such as logistic regression, K-nearest neighbor, decision tree, random forest, neural network and XGBoost. The experimental results show that random forest and XGBoost have significant advantages in predicting diabetes mellitus with high prediction accuracy. This study provides an effective tool for clinical applications and provides a reference for the development of future medical prediction models based on machine learning.