Research on Diabetes Prediction Based on Multiple machine Learning Methods

Abstract

Diabetes falls within the category of chronic diseases, and the issue of its prevention and control has always been a health concern for all mankind. This study constructs a prediction model for diabetes based on four machine learning methods: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). In the distribution of important features in LR, the top five important features are selected. On this basis, a model is constructed through other algorithms, aiming to reduce the interference of other irrelevant features, and then compare the performance of models constructed by different machine learning algorithms. The dataset of this study contains 768 samples, covering 8 characteristics including metrics like the number of pregnancies and plasma glucose concentration. Finally, the accuracy rates of the models constructed by the four algorithms of LR, DT, RF, and XGBoost were 75.97%, 78.57%, 74.03% and 74.68% respectively. Combining the area under the receiver operating characteristic curve (AUC), as well as the precision rate, recall rate, and F1 score of the positive category, it is ultimately concluded that the predictive effect of DT is the best. However, to better integrate diabetes prediction models with practical applications, more data and resources are still needed to support them.