KEYWORDS: Data modeling, Performance modeling, Machine learning, Education and training, Glucose, Deep learning, Feature selection, Diseases and disorders, Random forests, Plasma
In the field of medicine, disease prevention is more important than treatment. Diabetes, as one of the diseases that are harmful and have a large number of patients, the prediction of diabetes using learning models is an essential part of diabetes prevention and treatment in the future medical field. In this study, compound feature selection was used to screen out the eight features with the most predictive ability, and diabetes was predicted by six machine learning models and two deep learning models, and the final results were obtained as follows: the XGBoost classification had the best prediction performance, with an accuracy of 99.1%, a precision of 99.0%, a recall rate of 99.2%, an F1 score of 99.1%, and an AUC value of up to 0.991; CatBoost and LightGBM models have the next best performance. This is consistent with our previous findings. This study further confirms the value and potential of the XGBoost model for diabetes prediction, identifying superior feature selection methods compared to previous studies, and improving the predictive performance of the model while reducing model complexity when dealing with more complex data.
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