Analysis of Risk Factors and Prediction of Type 2 Diabetes Mellitus Based on Machine Learning

Corresponding author: Lu Bo, lubodf@163.com

DOI: 10.12201/bmr.202509.00003

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  Abstract: Purpose/Significance Aiming at the limitations of traditional evidence-based medicine (EBM) in dissecting the multi-factor interaction mechanisms of type 2 diabetes mellitus (T2DM), this study constructed a multidimensional data mining prediction framework to enhance the accuracy of risk assessment and the efficiency of clinical decision-making. Method/Process Based on the Pima dataset, univariate, bivariate, and multivariate analyses were conducted to screen core risk factors. Five machine learning models—LR, RF, SVM, XGBoost, and LightGBM—were employed for modeling. Hyperparameter optimization was performed using grid search and cross-validation. Result/Conclusion The identified key risk factors (e.g., blood glucose level, body mass index, and age) were consistent with conclusions from traditional EBM. Among the models, RF achieved the highest prediction accuracy of 0.8701, demonstrating superior overall performance. By integrating data mining and feature selection, this method significantly reduces data collection costs and shortens the cycle of risk factor identification, while revealing nonlinear interaction mechanisms among variables. It provides an efficient tool for community-based screening of high-risk populations.

  Key words: type 2 diabetes; risk prediction; multidimensional data mining; machine learning

  Submit time: 1 September 2025

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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