A Machine-Learning-Based Clinical Decision Model for Predicting Amputation Risk in Patients with Diabetic Foot Ulcers: Diagnostic Performance and Practical Implications.

ABSTRACT: Objective: To establish a reliable machine-learning-based model for predicting the risk of lower limb amputation in patients with diabetic foot ulcers and to provide quantitative evidence for clinical decision-making and individualized prevention strategies. Methods: This retrospective study analyzed data from 149 hospitalized diabetic foot ulcer patients treated at Beijing Shijitan Hospital between January 2019 and December 2022. Patients were divided into amputation and non-amputation groups according to clinical outcomes. Candidate predictors-including infection biomarkers, vascular parameters, and nutritional indices-were first screened using the least absolute shrinkage and selection operator algorithm. Subsequently, a support vector machine model was trained and interna

SUBMITTER: Gao L 

PROVIDER: S-EPMC12731953 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature