ArticleHealthcare (Basel, Switzerland)2021
Distal Symmetric Polyneuropathy Identification in Type 2 Diabetes Subjects: A Random Forest Approach.
Article in Healthcare (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 14 citations in OpenAlex.
- Optimizing Clinical Diabetes Diagnosis through Generative Adversarial Networks: Evaluation and Validation.Diseases (Basel, Switzerland) · 2023Article
- Article
- Applying an Improved Stacking Ensemble Model to Predict the Mortality of ICU Patients with Heart Failure.Journal of clinical medicine · 2022Article
- Using Machine Learning to Identify Organ System Specific Limitations to Exercise via Cardiopulmonary Exercise Testing.IEEE journal of biomedical and health informatics · 2022Article
- Prediction of Diabetic Sensorimotor Polyneuropathy Using Machine Learning Techniques.Journal of clinical medicine · 2021Article
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The prevalence of diabetes mellitus is increasing worldwide, causing health and economic implications. One of the principal microvascular complications of type 2 diabetes is Distal Symmetric Polyneuropathy (DSPN), affecting 42.6% of the population in Mexico. Therefore, the purpose of this study was to find out the predictors of this complication. The dataset contained a total number of 140 subjects, including clinical and paraclinical features. A multivariate analysis was constructed using Boruta as a feature selection method and Random Forest as a classification algorithm applying the strategy of K-Folds Cross Validation and Leave One Out Cross Validation. Then, the models were evaluated through a statistical analysis based on sensitivity, specificity, area under the curve (AUC) and receiving operating characteristic (ROC) curve. The results present significant values obtained by the model with this approach, presenting 67% of AUC with only three features as predictors. It is possible to conclude that this proposed methodology can classify patients with DSPN, obtaining a preliminary computer-aided diagnosis tool for the clinical area in helping to identify the diagnosis of DSPN.
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Registered trials
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