ArticleJournal of clinical medicine2023
Survival Prediction in Diabetic Foot Ulcers: A Machine Learning Approach.
Article in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Advances in the application of artificial intelligence-driven multi-modal imaging technologies in the comprehensive diagnosis and treatment of diabetic foot ulcers.Reviews in endocrine & metabolic disorders · 2026Review
- Evaluating risk factors affecting chronic wound healing: a comprehensive analysis using logistic regression and neural network models.Frontiers in health services · 2026Article
- Artificial Intelligence and Machine Learning in Diabetic Foot Ulcer Care: Advances in Diagnosis, Treatment, Prognosis, and Novel Therapeutic Strategies.Journal of diabetes science and technology · 2025Review
- Advancing Diabetic Foot Ulcer Care: AI and Generative AI Approaches for Classification, Prediction, Segmentation, and Detection.Healthcare (Basel, Switzerland) · 2025Review
- Prevalence of Thyrotropin Receptor Antibodies and Clinical Profile of Patients with Alopecia Areata: A Cross-Sectional Study.Skin appendage disorders · 2025Article
- Increased risk of major adverse cardiovascular events in patients with deep and infected diabetes-related foot ulcers.Diabetologia · 2025Article
- Machine Learning Algorithm-Aided Determination of Predictors of Mortality from Diabetic Foot Sepsis at a Regional Hospital in South Africa During the COVID-19 Pandemic.Medicina (Kaunas, Lithuania) · 2024Article
- The role of machine learning in advancing diabetic foot: a review.Frontiers in endocrinology · 2024Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Our paper proposes the first machine learning model to predict long-term mortality in patients with diabetic foot ulcers (DFUs). The study includes 635 patients with DFUs admitted from January 2007 to December 2017, with a follow-up period extending until December 2020. Two multilayer perceptron (MLP) classifiers were developed. The first MLP model was developed to predict whether the patient will die in the next 5 years after the current hospitalization. The second MLP classifier was built to estimate whether the patient will die in the following 10 years. The 5-year and 10-year mortality models were based on the following predictors: age; the University of Texas Staging System for Diabetic Foot Ulcers score; the Wagner-Meggitt classification; the Saint Elian Wound Score System; glomerular filtration rate; topographic aspects and the depth of the lesion; and the presence of foot ischemia, cardiovascular disease, diabetic nephropathy, and hypertension. The accuracy for the 5-year and 10-year models was 0.7717 and 0.7598, respectively (for the training set) and 0.7244 and 0.7087, respectively (for the test set). Our findings indicate that it is possible to predict with good accuracy the risk of death in patients with DFUs using non-invasive and low-cost predictors.
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