Observational studyMedical & biological engineering & computing2022
Machine learning algorithm to evaluate risk factors of diabetic foot ulcers and its severity.
Observational study in Medical & biological engineering & computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 5 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 5 syntheses or guidelines pooled it, 53 citations in OpenAlex.
- Prediction of Factors Influencing the Incidence of Diabetic Foot Ulcers Using Classical Statistical and Machine Learning Approaches: A Systematic Review.International wound journal · 2026Pooled it
- [Diabetic foot care: full-lifecycle management driven by digital intelligent technologies].Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery · 2026Pooled it
- Research progress on risk prediction models for the diabetic foot.Acta diabetologica · 2025Pooled it
- Pooled it
- Machine learning for diabetic foot care: accuracy trends and emerging directions in healthcare AI.Frontiers in public health · 2025Pooled it
- Prospective observational cohort of algorithm-guided reassessment in diabetic foot: real-world outcomes and prognostic associations.Frontiers in clinical diabetes and healthcare · 2026Article
- Precision nutrition in diabetic foot ulcers: multimodal artificial intelligence for personalized metabolic management.Frontiers in nutrition · 2026Review
- A Machine-Learning-Based Clinical Decision Model for Predicting Amputation Risk in Patients with Diabetic Foot Ulcers: Diagnostic Performance and Practical Implications.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial Intelligence for Diabetes Complication Prediction: A Systematic Review of Current Applications and Future Directions.Journal of diabetes science and technology · 2025Review
- Evaluation of Machine Learning Model Performance in Diabetic Foot Ulcer: Retrospective Cohort Study.JMIR medical informatics · 2025Article
- Prognostic value of the site, depth, and infection/ischemia classification system in diabetic foot ulcers: a retrospective cohort study.Scientific reports · 2025Article
- Distinct cytokine profiles associated with malnutrition, dyslipidemia and kidney dysfunction in patients with diabetic foot ulcer.Scientific reports · 2025Article
- Machine learning-based risk prediction model for neuropathic foot ulcers in patients with diabetic peripheral neuropathy.Journal of diabetes investigation · 2025Article
- Diabetic Foot Ulcers Detection Model Using a Hybrid Convolutional Neural Networks-Vision Transformers.Diagnostics (Basel, Switzerland) · 2025Article
- Diabetic peripheral neuropathy detection of type 2 diabetes using machine learning from TCM features: a cross-sectional study.BMC medical informatics and decision making · 2025Observational
- Development and validation of predictive models for diabetic retinopathy using machine learning.PloS one · 2025Article
- Artificial intelligence applied to diabetes complications: a bibliometric analysis.Frontiers in artificial intelligence · 2025Article
- Artificial intelligence's suggestions for level of amputation in diabetic foot ulcers are highly correlated with those of clinicians, only with exception of hindfoot amputations.International wound journal · 2024Article
- Combined with dynamic serum proteomics and clinical follow-up to screen the serum proteins to promote the healing of diabetic foot ulcer.Endocrine · 2024Article
- Personalized prediction of diabetic foot ulcer recurrence in elderly individuals using machine learning paradigms.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early identification of the risk factors associated with development of diabetic foot ulcer (DFU) can be facilitated using machine learning techniques. The aim of this study is to find out the association of various clinical and biochemical risk factors with DFU and develop a prediction model using different machine learning algorithms. Eighty each of type 2 diabetes mellitus (T2DM) with DFU and (T2DM) without DFU were enrolled for this observational study. Clinical and laboratory data were analysed using different machine learning algorithms: Support vector machines (SVM-Poly K), Naive Bayes (NB), K-nearest neighbour (KNN), random forest (RF) and three ensemble learners: Stacking C, Bagging and AdaBoost for constructing prediction models for discriminating between the two groups (stage I classification) and ulcer type classification (stage II classification). Ensemble learning performed better than individual classifiers in terms of various performance evaluation metrics. New risk factors like ApoA1 and IL-10 for development of DFU in diabetes mellitus were identified. IL-10 along with uric acid could discriminate the grades of ulcers according to its severity. Decision fusion strategy using Stacking C algorithm resulted in enhanced prediction accuracy for both the stages of classification which can be used as a complementary method for computational screening for DFU and its subtypes. Current methodology for T2DM with DFU/T2DM without DFU and ulcer type classification.
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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.