ArticleTechnology and health care : official journal of the European Society for Engineering and Medicine2024
Personalized prediction of diabetic foot ulcer recurrence in elderly individuals using machine learning paradigms.
Article in Technology and health care : official journal of the European Society for Engineering and Medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
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Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Research progress on risk prediction models for the diabetic foot.Acta diabetologica · 2025Pooled it
- Machine learning for diabetic foot care: accuracy trends and emerging directions in healthcare AI.Frontiers in public health · 2025Pooled it
- 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
- Predicting major amputation risk in diabetic foot ulcers using comparative machine learning models for enhanced clinical decision-making.Scientific reports · 2025Article
- Advancing Diabetic Foot Ulcer Care: AI and Generative AI Approaches for Classification, Prediction, Segmentation, and Detection.Healthcare (Basel, Switzerland) · 2025Review
- A feature explainability-based deep learning technique for diabetic foot ulcer identification.Scientific reports · 2025Article
- Artificial intelligence applied to diabetes complications: a bibliometric analysis.Frontiers in artificial intelligence · 2025Article
- Enhancing diabetic foot ulcer prediction with machine learning: A focus on Localized examinations.Heliyon · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study utilizes machine learning to analyze the recurrence risk of diabetic foot ulcers (DFUs) in elderly diabetic patients, aiming to enhance prevention and intervention efforts.
objectiveThe goal is to construct accurate predictive models for assessing the recurrence risk of DFUs based on high-risk factors, such as age, blood sugar control, alcohol consumption, and smoking, in elderly diabetic patients.
methodsData from 138 elderly diabetic patients were collected, and after data cleaning, outlier screening, and feature integration, machine learning models were constructed. Support Vector Machine (SVM) was employed, achieving an accuracy rate of 93%.
resultsExperimental results demonstrate the effectiveness of SVM in predicting the recurrence risk of DFUs in elderly diabetic patients, providing clinicians with a more accurate tool for assessment.
conclusionsThe study highlights the significance of machine learning in managing foot ulcers in elderly diabetic patients, particularly in predicting recurrence risk. This approach facilitates timely intervention, reducing the likelihood of patient recurrence, and introduces computer-assisted medical strategies in elderly diabetes management.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.