Scholarlink

<p>Diabetic foot is a severe chronic complication of diabetes, mainly resulting from peripheral neuropathy and vasculopathy, and may progress to ulcers, infections, and amputation if not treated in a timely manner. Early identification of patients who already present a high-risk diabetic foot profile at clinical evaluation is therefore critical for guiding preventive interventions. This study aimed to develop an interpretable machine learning–based risk classification platform for classifying current diabetic foot risk status in patients with diabetes and supporting clinical risk stratification and personalized care. The model was not designed to predict future ulcer occurrence, amputation, or wound healing outcomes. In this retrospective study, data from 1938 diabetic patients at Nanfang Hospital of Southern Medical University were used for model development, and an independent external validation cohort of 695 diabetic patients from Shanghai Changhai Hospital was used to assess generalizability. Fifty clinical features covering demographic, metabolic, vascular, neurological, inflammatory, renal, and diabetes-related factors were included. Ten machine learning models were developed and compared. Recursive Feature Elimination (RFE) was applied to the top-performing models for feature selection, and SHapley Additive exPlanations (SHAP) was used to interpret model predictions and construct a diabetic foot risk classification platform. Using sixteen selected features, the CatBoost model achieved the best performance on the internal test set, with an AUC of 0.935 ± 0.016 and accuracy, precision, and recall of 0.88. In the external validation cohort, the model maintained stable performance, with an AUC of 0.922 ± 0.006 and accuracy, precision, recall, and F1-score all equal to 0.88, demonstrating good generalizability. We developed a CatBoost-based diabetic foot risk classification model incorporating sixteen clinically accessible features. The model showed stable and reliable performance across internal and external cohorts and remained robust under data noise and class imbalance, supporting its potential utility for real-world clinical risk classification and early preventive care.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An interpretable machine learning model for diabetic foot risk classification in patients with diabetes

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月23日

查看全文
  • Xiaohua Hu,
  • Huijun Xi,
  • Zibo Fan,
  • Leixia Zhu,
  • Jia Shi,
  • Shuangshuang Qian,
  • Jing Qiu,
  • Yingzhi Li,
  • Hong Ding

摘要

Diabetic foot is a severe chronic complication of diabetes, mainly resulting from peripheral neuropathy and vasculopathy, and may progress to ulcers, infections, and amputation if not treated in a timely manner. Early identification of patients who already present a high-risk diabetic foot profile at clinical evaluation is therefore critical for guiding preventive interventions. This study aimed to develop an interpretable machine learning–based risk classification platform for classifying current diabetic foot risk status in patients with diabetes and supporting clinical risk stratification and personalized care. The model was not designed to predict future ulcer occurrence, amputation, or wound healing outcomes. In this retrospective study, data from 1938 diabetic patients at Nanfang Hospital of Southern Medical University were used for model development, and an independent external validation cohort of 695 diabetic patients from Shanghai Changhai Hospital was used to assess generalizability. Fifty clinical features covering demographic, metabolic, vascular, neurological, inflammatory, renal, and diabetes-related factors were included. Ten machine learning models were developed and compared. Recursive Feature Elimination (RFE) was applied to the top-performing models for feature selection, and SHapley Additive exPlanations (SHAP) was used to interpret model predictions and construct a diabetic foot risk classification platform. Using sixteen selected features, the CatBoost model achieved the best performance on the internal test set, with an AUC of 0.935 ± 0.016 and accuracy, precision, and recall of 0.88. In the external validation cohort, the model maintained stable performance, with an AUC of 0.922 ± 0.006 and accuracy, precision, recall, and F1-score all equal to 0.88, demonstrating good generalizability. We developed a CatBoost-based diabetic foot risk classification model incorporating sixteen clinically accessible features. The model showed stable and reliable performance across internal and external cohorts and remained robust under data noise and class imbalance, supporting its potential utility for real-world clinical risk classification and early preventive care.