Comparative and Explanatory Analysis of Influencing Factor Models for Type 2 Diabetes Mellitus Complicated with Hypertension Based on Machine Learning

Corresponding author: ruichen, ruichen666666@163.com

DOI: 10.12201/bmr.202511.00058

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  Abstract: Objective/Significance To identify the key influencing factors of type 2 diabetes mellitus complicated with hypertension using machine learning and SHAP (SHapley Additive exPlanations) interpretive analysis, and to provide a basis for risk stratification and personalized intervention in high-risk populations. Methods/Process A total of 3839 inpatients admitted to the Central Hospital of C City from 2020 to 2022 were enrolled. The cohort was randomly divided into a training set and a test set at a ratio of 7:3. Four predictive models for comorbidity influencing factors, including Random Forest (RF), Support Vector Machine (SVM), XGBoost, and NGBoost, were constructed and comparatively analyzed. Additionally, 1000 inpatients from the same department in 2023 were recruited as an independent validation set. The model performance was evaluated through SHAP analysis combined with age-stratified subgroup validation. Results/Conclusions The NGBoost model demonstrated the optimal performance, achieving an accuracy of 0.9010, sensitivity of 0.8868, specificity of 0.9096, F1-score of 0.8707, and area under the receiver operating characteristic curve (AUC) of 0.9671 in the training and test sets. In the independent validation set, the model yielded an accuracy of 0.9184, sensitivity of 0.9145, specificity of 0.9207, F1-score of 0.8939, and AUC of 0.9745. SHAP analysis revealed that smoking, the number of complications, and thyroid diseases were the prominent influencing factors. Furthermore, the NGBoost model exhibited excellent stability across four age subgroups. This study clarifies the relative importance of various influencing factors, which is conducive to the formulation of effective clinical treatment and management strategies for T2DM complicated with hypertension.

  Key words: Diabetes and Hypertension Comorbidity; T2DM; Hypertension; SHAP analysis; Machine Learning

  Submit time: 20 November 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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