Machine learning-based prediction of major amputation risk after initial limb-preserving surgery in diabetic foot.

Authors

Kerim Bora Yilmaz, Gokalp Tulum, Ferhat Cuce, Zehra Simsek, Necibe Sare Mert, Sema Horasan Hatipoglu, Cansu Bozkurt, Muhammet Ikbal Isik, Ali Murat Basak, Onur Osman

Published in

Frontiers in endocrinology. Volume 17. Pages 1889329. Epub Aug 03, 2026.

Abstract

Accurate preoperative prediction of whether an initially limb-preserving strategy in diabetic foot management will culminate in minor or major amputation remains a clinical challenge. This study aimed to develop and evaluate using two temporally separated cohorts a machine-learning framework using routinely available baseline clinical, laboratory, and selected imaging and vascular variables.
Two temporally separated cohorts were used, with Dataset 1 for model development and Dataset 2 for temporally separated evaluation. A 20-repetition stratified outer-split workflow was implemented, incorporating two-step feature selection, Optuna-based hyperparameter optimization, training-only SMOTE, and threshold tuning to maximize the F2-score under a recall constraint of ≥0.70. Six classifiers were evaluated using average precision (AP), ROC-AUC, recall, precision, specificity, accuracy, and Brier score.
The major-amputation group exhibited a more severe baseline phenotype, including higher inflammatory burden, worse neuropathy and wound severity, and a higher prevalence of necrotizing fasciitis. Internally, multilayer perceptron achieved the highest AP (55.9% ± 13.6%). In external evaluation, k-nearest neighbors achieved the highest AP (65.1% ± 10.2%) and recall (72.8% ± 19.6%), whereas multilayer perceptron showed higher precision and specificity. Key contributors included necrotizing fasciitis, neuropathy severity, hemoglobin, PEDIS classification, and inflammatory indices.
These findings suggest that prediction of amputation level is feasible, validated in a temporally separated cohort, and clinically interpretable, and may support future decision-support applications, although further validation is required before clinical implementation.

PMID:
42609499
Bibliographic data and abstract were imported from PubMed on 18 Aug 2026.

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