Retrospective Study
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Diabetes. Jul 15, 2025; 16(7): 104789
Published online Jul 15, 2025. doi: 10.4239/wjd.v16.i7.104789
Predictive model and risk analysis for outcomes in diabetic foot ulcer using eXtreme Gradient Boosting algorithm and SHapley Additive exPlanation
Lei Gao, Zi-Xuan Liu, Jiang-Ning Wang
Lei Gao, Jiang-Ning Wang, Department of Orthopedics Surgery, Beijing Shijitan Hospital Affiliated to Capital Medical University, Beijing 100038, China
Zi-Xuan Liu, Department of Clinical Medicine, Capital Medical University, Beijing 100038, China
Co-first authors: Lei Gao and Zi-Xuan Liu.
Author contributions: Gao L conducted experiments and guided the writing of the manuscript; Liu ZX collected and analyzed the data; Wang JN is the corresponding author of this manuscript; Wang JN designed the experimental program and chaired the seminar. This manuscript has been read and approved by all the co-authors. Gao L and Liu ZX contributed equally to this work and they are co-first authors.
Institutional review board statement: This study was performed in accordance with the International Conference on Harmonization Good Clinical Practice Guidelines and the Declaration of Helsinki and approved by the Ethical Review Committee of Beijing Shijitan Hospital, Capital Medical University, No. IIT2025-049-001.
Informed consent statement: This study was a retrospective analysis of anonymized clinical data. Ethical approval was obtained from the Institutional Review Board, and the requirement for informed consent was waived.
Conflict-of-interest statement: The authors declare that they have no conflicts of interest.
Data sharing statement: The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Jiang-Ning Wang, MD, Chief Physician, Department of Orthopedics Surgery, Beijing Shijitan Hospital Affiliated to Capital Medical University, No. 10 Tieyi Road, Yangfangdian, Haidian District, Beijing 100038, China. wangjn@bjsjth.cn
Received: January 2, 2025
Revised: April 22, 2025
Accepted: June 13, 2025
Published online: July 15, 2025
Processing time: 195 Days and 7 Hours
Abstract
BACKGROUND

Diabetic foot ulcer (DFU) is a serious and destructive complication of diabetes, which has a high amputation rate and carries a huge social burden. Early detection of risk factors and intervention are essential to reduce amputation rates. With the development of artificial intelligence technology, efficient interpretable predictive models can be generated in clinical practice to improve DFU care.

AIM

To develop and validate an interpretable model for predicting amputation risk in DFU patients.

METHODS

This retrospective study collected basic data from 599 patients with DFU in Beijing Shijitan Hospital between January 2015 and June 2024. The data set was randomly divided into a training set and test set with fivefold cross-validation. Three binary variable models were built with the eXtreme Gradient Boosting (XGBoost) algorithm to input risk factors that predict amputation probability. The model performance was optimized by adjusting the super parameters. The predictive performance of the three models was expressed by sensitivity, specificity, positive predictive value, negative predictive value and area under the curve (AUC). Visualization of the prediction results was realized through SHapley Additive exPlanation (SHAP).

RESULTS

A total of 157 (26.2%) patients underwent minor amputation during hospitalization and 50 (8.3%) had major amputation. All three XGBoost models demonstrated good discriminative ability, with AUC values > 0.7. The model for predicting major amputation achieved the highest performance [AUC = 0.977, 95% confidence interval (CI): 0.956-0.998], followed by the minor amputation model (AUC = 0.800, 95%CI: 0.762-0.838) and the non-amputation model (AUC = 0.772, 95%CI: 0.730-0.814). Feature importance ranking of the three models revealed the risk factors for minor and major amputation. Wagner grade 4/5, osteomyelitis, and high C-reactive protein were all considered important predictive variables.

CONCLUSION

XGBoost effectively predicts diabetic foot amputation risk and provides interpretable insights to support personalized treatment decisions.

Keywords: Diabetic foot ulcer; Amputation risk stratification; Clinical risk prediction; eXtreme Gradient Boosting; SHapley Additive exPlanation; Machine learning

Core Tip: This study developed and validated an eXtreme Gradient Boosting-based predictive model for stratifying amputation risk in patients with diabetic foot ulcers. By integrating 29 clinical variables and applying SHapley Additive exPlanation for interpretability, the model achieved high predictive accuracy, especially for major amputations (area under the curve = 0.977). Key predictors included Wagner grade, albumin, infection markers, and vascular intervention. The model allows for the early identification of high-risk patients and supports individualized treatment decisions, offering a clinically interpretable tool for improving diabetic foot management.