ArticleInternational journal of molecular sciences2023
Machine Learning as a Support for the Diagnosis of Type 2 Diabetes.
Article in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 59 citations in OpenAlex.
- Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis.Frontiers in digital health · 2025Pooled it
- Development and validation of a novel interpretable machine learning model integrating immune-inflammatory indicators for intravenous immunoglobulin resistance in Kawasaki disease.Translational pediatrics · 2026Article
- Explainable Meta-Learning Ensemble Framework for Predicting Insulin Dose Adjustments in Diabetic Patients: A Comparative Machine Learning Approach with SHAP-Based Clinical Interpretability.Medicina (Kaunas, Lithuania) · 2026Article
- Predicting 3-year depressive symptoms among middle-aged and older adults in rural China using random forest: insights from the China health and retirement longitudinal study.BMC psychology · 2025Article
- Protocol for an Integrative Meta-Analysis of the Application of Machine Learning Algorithms in the Prediction of Chronic Disease Risks and Outcomes.Chronic diseases and translational medicine · 2025Article
- Prediction of Mini-Mental State Examination Scores for Cognitive Impairment and Machine Learning Analysis of Oral Health and Demographic Data Among Individuals Older Than 60 Years: Cross-Sectional Study.JMIR medical informatics · 2025Article
- Multi-modal analyses of proteomic measurements associated with type 2 diabetes from the Project Baseline Health Study.Communications medicine · 2025Article
- Federated multimodal AI for precision-equitable diabetes care.Frontiers in digital health · 2025Review
- Robust diabetic prediction using ensemble machine learning models with synthetic minority over-sampling technique.Scientific reports · 2024Article
- Setting Ranges in Potential Biomarkers for Type 2 Diabetes Mellitus Patients Early Detection By Sex-An Approach with Machine Learning Algorithms.Diagnostics (Basel, Switzerland) · 2024Article
- Article
- Artificial intelligence in endocrinology: a comprehensive review.Journal of endocrinological investigation · 2024Review
- Article
- Classification and prediction of the effects of nutritional intake on diabetes mellitus using artificial neural network sensitivity analysis: 7th Korea National Health and Nutrition Examination Survey.Nutrition research and practice · 2023Article
- Using Artificial Intelligence to Predict the Development of Kyphosis Disease: A Systematic Review.Cureus · 2023Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetes is a chronic, metabolic disease characterized by high blood sugar levels. Among the main types of diabetes, type 2 is the most common. Early diagnosis and treatment can prevent or delay the onset of complications. Previous studies examined the application of machine learning techniques for prediction of the pathology, and here an artificial neural network shows very promising results as a possible valuable aid in the management and prevention of diabetes. Additionally, its superior ability for long-term predictions makes it an ideal choice for this field of study. We utilized machine learning methods to uncover previously undiscovered associations between an individual's health status and the development of type 2 diabetes, with the goal of accurately predicting its onset or determining the individual's risk level. Our study employed a binary classifier, trained on scratch, to identify potential nonlinear relationships between the onset of type 2 diabetes and a set of parameters obtained from patient measurements. Three datasets were utilized, i.e., the National Center for Health Statistics' (NHANES) biennial survey, MIMIC-III and MIMIC-IV. These datasets were then combined to create a single dataset with the same number of individuals with and without type 2 diabetes. Since the dataset was balanced, the primary evaluation metric for the model was accuracy. The outcomes of this study were encouraging, with the model achieving accuracy levels of up to 86% and a ROC AUC value of 0.934. Further investigation is needed to improve the reliability of the model by considering multiple measurements from the same patient over time.
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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.