Hypoglycemia is a serious complication in individuals with type 2 diabetes mellitus. Identifying who is most at risk remains challenging due to the non-linear relationships between hypoglycemia and its associated risk factors. The objective of this study is to evaluate the importance and impact of risk factors related to the incidence of hypoglycemia through an explainable machine learning method. This prospective study enrolled 1306 adults with type 2 diabetes mellitus at a specialized diabetes center. Over three months, participants were asked to do self-monitoring blood glucose measurements and record hypoglycemic events. Nine clinically relevant features were analyzed using five machine learning models. The performance of the models was evaluated by different metrics. The SHapley Additive exPlanation method was used to elucidate how each covariate influenced the risk of hypoglycemia. Overall, 419 participants (32.08%) reported at least one hypoglycemic episode. Our findings highlight the non-linear nature of hypoglycemia risk in individuals with T2DM. Insulin therapy, Diabetes duration (> 13.7 years), and eGFR (< 60.2 mL/min/1.73 m²) were the most important predictors of hypoglycemia, followed by age, HbA1C, triglycerides, total cholesterol, gender, and BMI.

Keywords: Hypoglycemia prediction; Machine learning; SHAP; Type 2 diabetes mellitus.

© 2025. The Author(s).

低血糖是2型糖尿病患者的一个严重并发症。由于低血糖与其相关风险因素之间的非线性关系，识别高危人群仍然具有挑战性。本研究的目的是通过可解释的机器学习方法评估与低血糖发生率相关的风险因素的重要性及其影响。这项前瞻性研究招募了1306名患有2型糖尿病的成人，并在一家专业的糖尿病中心进行。三个月内，参与者被要求进行自我监测血糖测量并记录低血糖事件。使用五种机器学习模型分析了九个临床相关特征。通过不同的指标评估模型的表现。SHapley Additive exPlanation（SHAP）方法用于阐明每个协变量如何影响低血糖的风险。总体而言，419名参与者（32.08%）报告至少一次低血糖发作。我们的研究结果强调了T2DM患者中低血糖风险的非线性性质。胰岛素治疗、糖尿病病程（> 13.7年）和eGFR（

关键词： 低血糖预测；机器学习；SHAP；2型糖尿病

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