ArticleDiagnostics (Basel, Switzerland)2021
Performance Analysis of Conventional Machine Learning Algorithms for Diabetic Sensorimotor Polyneuropathy Severity Classification.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for detection, prediction, and severity classification of diabetic peripheral neuropathy: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Electrophysiological-based automatic subgroups diagnosis of patients with chronic dysimmune polyneuropathies.Journal of neuroengineering and rehabilitation · 2025Article
- Clinical Phenotypes of Diabetic Peripheral Neuropathy: Implications for Phenotypic-Based Therapeutics Strategies.Diabetes & metabolism journal · 2025Review
- Estimating the Risk of Lower Extremity Complications in Adults Newly Diagnosed With Diabetic Polyneuropathy: Retrospective Cohort Study.JMIR diabetes · 2025Article
- Article
- Use of machine learning algorithms to construct models of symptom burden cluster risk in breast cancer patients undergoing chemotherapy.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2025Article
- Interpretable multimodal machine learning (IMML) framework reveals pathological signatures of distal sensorimotor polyneuropathy.Communications medicine · 2024Article
- Prediction Model for Polyneuropathy in Recent-Onset Diabetes Based on Serum Neurofilament Light Chain, Fibroblast Growth Factor-19 and Standard Anthropometric and Clinical Variables.Diabetes/metabolism research and reviews · 2024Article
- Article
- A Machine Learning-Based Severity Prediction Tool for the Michigan Neuropathy Screening Instrument.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes.Journal of diabetes science and technology · 2023Article
- Nomogram-Based Chronic Kidney Disease Prediction Model for Type 1 Diabetes Mellitus Patients Using Routine Pathological Data.Journal of personalized medicine · 2022Article
- Classification of painful or painless diabetic peripheral neuropathy and identification of the most powerful predictors using machine learning models in large cross-sectional cohorts.BMC medical informatics and decision making · 2022Article
- Machine Learning-Based Diabetic Neuropathy and Previous Foot Ulceration Patients Detection Using Electromyography and Ground Reaction Forces during Gait.Sensors (Basel, Switzerland) · 2022Article
- Performance Analysis of Conventional Machine Learning Algorithms for Diabetic Sensorimotor Polyneuropathy Severity Classification Using Nerve Conduction Studies.Computational intelligence and neuroscience · 2022Article
- Performance Analysis of Conventional Machine Learning Algorithms for Identification of Chronic Kidney Disease in Type 1 Diabetes Mellitus Patients.Diagnostics (Basel, Switzerland) · 2021Article
- Prediction of Diabetic Sensorimotor Polyneuropathy Using Machine Learning Techniques.Journal of clinical medicine · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundDiabetic peripheral neuropathy (DSPN), a major form of diabetic neuropathy, is a complication that arises in long-term diabetic patients. Even though the application of machine learning (ML) in disease diagnosis is a very common and well-established field of research, its application in diabetic peripheral neuropathy (DSPN) diagnosis using composite scoring techniques like Michigan Neuropathy Screening Instrumentation (MNSI), is very limited in the existing literature.
methodIn this study, the MNSI data were collected from the Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials. Two different datasets with different MNSI variable combinations based on the results from the eXtreme Gradient Boosting feature ranking technique were used to analyze the performance of eight different conventional ML algorithms.
resultsThe random forest (RF) classifier outperformed other ML models for both datasets. However, all ML models showed almost perfect reliability based on Kappa statistics and a high correlation between the predicted output and actual class of the EDIC patients when all six MNSI variables were considered as inputs.
conclusionsThis study suggests that the RF algorithm-based classifier using all MNSI variables can help to predict the DSPN severity which will help to enhance the medical facilities for diabetic patients.
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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.