Construction and validation of a hypoglycemia risk prediction model for hospitalized type 2 diabetes patients based on machine learning

作者：Caixia Liu, Zhaoxu Huang, T Liu, Yongjie Ge, Jie Yuan, Yue Lin, Chunli Wang, Jinjuan Zhang, Xiaoli Wang, Yan Hua, Rui Qi · 发表于：BMC Endocrine Disorders · 年份：2025 · DOI：10.1186/s12902-025-02104-x · 被引用次数：5 · 研究领域：Hyperglycemia and glycemic control in critically ill and hospitalized patients、Diabetes Management and Research、Diabetes Treatment and Management

BACKGROUND: To compare three machine learning algorithms for constructing a hypoglycemia risk prediction model in hospitalized type 2 diabetes patients, identify the optimal model, and validate it to provide decision-making support for early clinical identification of high-risk patients. METHODS: A case-control study design was adopted, retrospectively collecting clinical data from 1,167 hospitalized type 2 diabetes patients in the endocrinology department of a tertiary hospital from January to December 2024. Patients were divided into a hypoglycemia group (220 cases) and a non-hypoglycemia group (947 cases). After screening predictive variables using LASSO regression, the data were randomly split into a training set (934 cases) and a validation set (233 cases) at an 8:2. The training set was used to construct prediction models using Logistic Regression, Random Forest (RF), and Extreme Gradient Boosting (XGBoost) algorithms, with internal validation performed on the validation set to assess predictive performance. The optimal model was determined by comprehensively evaluating the Area Under the ROC Curve (AUC) and F1 score. The SHAP (Shapley Additive Explanations) method was applied for interpretability analysis. RESULTS: The incidence of hypoglycemia was 18.85% (220/1,167). LASSO regression identified nine key predictive variables: random C-peptide, insulin-containing fluid infusion, BMI, length of hospital stay, age, renal dysfunction, albumin level, lipohypertrophy, and in...