An interpreting machine learning models to predict amputation risk in patients with diabetic foot ulcers: a multi-center study.

ABSTRACT:

Background

Diabetic foot ulcers (DFUs) constitute a significant complication among individuals with diabetes and serve as a primary cause of nontraumatic lower-extremity amputation (LEA) within this population. We aimed to develop machine learning (ML) models to predict the risk of LEA in DFU patients and used SHapley additive explanations (SHAPs) to interpret the model.

Methods

In this retrospective study, data from 1,035 patients with DFUs at Sun Yat-sen Memorial Hospital were utilized as the training cohort to develop the ML models. Data from 297 patients across multiple tertiary centers were used for external validation. We then used least absolute shrinkage and selection operator analysis to identify predictors of amputation. We developed five ML models [logistic regres

SUBMITTER: Tao H 

PROVIDER: S-EPMC11975565 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature