BMJ open diabetes research & care · 2025
Interpretable machine learning model for predicting recurrence in patients with diabetic foot ulcers.
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Mou Weijiao, Shan Waiping, Yu Shiyan, Rui Shunli, Du Chenzhen, Huo Zhiqiang, Gu Haotian, Armstrong David G, Tang Dongfeng, Wang Yanzhong, Ayis Salma, Chen Lu, Yang Cheng, Deng Wuquan, International "Flow and Toe" Research Team (iFORT)
Аннотация
BACKGROUND: Diabetic foot ulcer (DFU) is a severe complication of diabetes mellitus, often characterized by a chronic disease course and a high recurrence rate, posing significant challenges to patient management. Accurately predicting DFU recurrence is essential for enhancing patient care and outcomes through timely treatment and intervention. This study aimed to develop a machine learning (ML) model to predict the 3-year recurrence risk in patients with DFU.
METHODS: A total of 494 patients with DFU were included and assigned to a training set and a test set at a 4:1 ratio. Four feature selection methods-least absolute shrinkage and selection operator, minimum redundancy maximum relevance, Fisher score and recursive feature elimination-were applied to the training set, and intersecting features were selected to construct the final feature set. Seven ML algorithms, including logistic regression, support vector machine, random forest, gradient boosting decision tree, AdaBoost, extreme gradient boosting (XGBoost) and light gradient boosting machine, were employed to develop predictive models. The models' parameters were optimized using fivefold cross-validation. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). The best-performing model was calibrated using Platt scaling, with calibration performance assessed by the Brier score. ML model interpretability was enhanced using SHapley Additive exPlanations (SHAP) analysis.
RESULTS: The XGBoost model demonstrated superior predictive performance, achieving an AUROC of 0.924 (95% CI 0.867 to 0.967). Following calibration with Platt scaling, the model exhibited a Brier score of 0.096, indicating good calibration. SHAP analysis identified key risk factors that aligned with existing literature and clinical expertise, further validating the model's interpretability and clinical relevance.
CONCLUSION: The XGBoost model demonstrated strong predictive accuracy and clinical relevance in assessing DFU recurrence risk. However, further multicenter validation with a larger sample size is needed to improve its generalizability and clinical applicability.
METHODS: A total of 494 patients with DFU were included and assigned to a training set and a test set at a 4:1 ratio. Four feature selection methods-least absolute shrinkage and selection operator, minimum redundancy maximum relevance, Fisher score and recursive feature elimination-were applied to the training set, and intersecting features were selected to construct the final feature set. Seven ML algorithms, including logistic regression, support vector machine, random forest, gradient boosting decision tree, AdaBoost, extreme gradient boosting (XGBoost) and light gradient boosting machine, were employed to develop predictive models. The models' parameters were optimized using fivefold cross-validation. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). The best-performing model was calibrated using Platt scaling, with calibration performance assessed by the Brier score. ML model interpretability was enhanced using SHapley Additive exPlanations (SHAP) analysis.
RESULTS: The XGBoost model demonstrated superior predictive performance, achieving an AUROC of 0.924 (95% CI 0.867 to 0.967). Following calibration with Platt scaling, the model exhibited a Brier score of 0.096, indicating good calibration. SHAP analysis identified key risk factors that aligned with existing literature and clinical expertise, further validating the model's interpretability and clinical relevance.
CONCLUSION: The XGBoost model demonstrated strong predictive accuracy and clinical relevance in assessing DFU recurrence risk. However, further multicenter validation with a larger sample size is needed to improve its generalizability and clinical applicability.