Interpretable machine learning model for predicting recurrence in patients with diabetic foot ulcers.

ABSTRACT:

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 i

SUBMITTER: Mou W 

PROVIDER: S-EPMC12612723 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature