ArticleBMC medical informatics and decision making2023
Study on risk factors of diabetic peripheral neuropathy and establishment of a prediction model by machine learning.
Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 37 citations in OpenAlex.
- Artificial intelligence for detection, prediction, and severity classification of diabetic peripheral neuropathy: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Machine learning in the prediction of diabetic peripheral neuropathy: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Smartphone-based App to Assess Diabetic Peripheral Neuropathy.medRxiv : the preprint server for health sciences · 2026Article
- Genetic polymorphisms in DNA repair gene XRCC1 and the risk of diabetic polyneuropathy.Scientific reports · 2026Article
- CINPred: a risk prediction tool for cervical intraepithelial neoplasia.Frontiers in oncology · 2026Article
- An Algorithm for Identifying Microvascular Complications in Type 1 Diabetes in the Clinical Setting: Development and External Validation.Health data science · 2026Article
- Development and Validation of a Machine Learning-Based Predictive Model for Peripheral Neuropathy Risk in Elderly Patients with Type 2 Diabetes.Risk management and healthcare policy · 2026Article
- The role of clinical laboratory parameters in diabetic neuropathy: correlations and recent advances.Frontiers in endocrinology · 2026Review
- Machine Learning-Based Prediction Model for Type 2 Diabetic Peripheral Neuropathy: Role of Bilateral Brachial-Ankle Pulse Wave Velocity and Anthropometric Indicators.International journal of general medicine · 2026Article
- Machine learning models predict mortality risk in diabetic neuropathy patients using MIMIC-IV data.Scientific reports · 2025Article
- Development and validation of a predictive model for diabetic peripheral neuropathy with type 2 diabetes mellitus in Xinjiang, China.Scientific reports · 2025Article
- Systematic review and critical appraisal of predictive models for diabetic peripheral neuropathy: Existing challenges and proposed enhancements.World journal of diabetes · 2025Article
- Article
- Machine learning-driven identification of key risk factors for predicting depression among nurses.BMC nursing · 2025Article
- Research Progress of Bioactive Peptides in Improving Type II Diabetes.Foods (Basel, Switzerland) · 2025Review
- Predict and prevent microvascular complications of type 2 diabetes: a cross-sectional and longitudinal study in Chinese communities.Frontiers in endocrinology · 2025Article
- Artificial intelligence applied to diabetes complications: a bibliometric analysis.Frontiers in artificial intelligence · 2025Article
- Machine learning-based prediction of diabetic peripheral neuropathy: model development and clinical validation.Frontiers in endocrinology · 2025Article
- Development and Validation of Machine Learning Models for Identifying Prediabetes and Diabetes in Normoglycemia.Diabetes/metabolism research and reviews · 2024Article
- Relationship of lncRNA FTX and miR-186-5p levels with diabetic peripheral neuropathy in type 2 diabetes and its bioinformatics analysis.Irish journal of medical science · 2024Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDiabetic peripheral neuropathy (DPN) is a common complication of diabetes. Predicting the risk of developing DPN is important for clinical decision-making and designing clinical trials.
methodsWe retrospectively reviewed the data of 1278 patients with diabetes treated in two central hospitals from 2020 to 2022. The data included medical history, physical examination, and biochemical index test results. After feature selection and data balancing, the cohort was divided into training and internal validation datasets at a 7:3 ratio. Training was made in logistic regression, k-nearest neighbor, decision tree, naive bayes, random forest, and extreme gradient boosting (XGBoost) based on machine learning. The k-fold cross-validation was used for model assessment, and the accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC) were adopted to validate the models' discrimination and clinical practicality. The SHapley Additive exPlanation (SHAP) was used to interpret the best-performing model.
resultsThe XGBoost model outperformed other models, which had an accuracy of 0·746, precision of 0·765, recall of 0·711, F1-score of 0·736, and AUC of 0·813. The SHAP results indicated that age, disease duration, glycated hemoglobin, insulin resistance index, 24-h urine protein quantification, and urine protein concentration were risk factors for DPN, while the ratio between 2-h postprandial C-peptide and fasting C-peptide(C2/C0), total cholesterol, activated partial thromboplastin time, and creatinine were protective factors.
conclusionsThe machine learning approach helped established a DPN risk prediction model with good performance. The model identified the factors most closely related to DPN.
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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.