ArticleDiabetes & metabolism journal2022
Development of Various Diabetes Prediction Models Using Machine Learning Techniques.
Article in Diabetes & metabolism journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 26 citations in OpenAlex.
- Machine learning-driven risk prediction for post-hospitalization diabetes case management: Integrating clinical and social determinants of health.International journal of medical informatics · 2026Article
- Associations between arginine metabolism and gestational diabetes mellitus: an exploratory longitudinal study using widely targeted metabolomics.BMC pregnancy and childbirth · 2026Article
- Machine learning predicts diabetes risk in high-risk populations: analysis of National Health and Nutrition Examination Survey data.Archives of medical science : AMS · 2026Article
- Applications of Artificial Intelligence and Machine Learning in Prediabetes: A Scoping Review.Journal of diabetes science and technology · 2025Review
- Artificial intelligence applied to diabetes complications: a bibliometric analysis.Frontiers in artificial intelligence · 2025Article
- Federated multimodal AI for precision-equitable diabetes care.Frontiers in digital health · 2025Review
- Use of Machine Learning to Predict the Incidence of Type 2 Diabetes Among Relatively Healthy Adults: A 10-Year Longitudinal Study in Taiwan.Diagnostics (Basel, Switzerland) · 2024Article
- Predicting diabetes in adults: identifying important features in unbalanced data over a 5-year cohort study using machine learning algorithm.BMC medical research methodology · 2024Article
- Validation of the Framingham Diabetes Risk Model Using Community-Based KoGES Data.Journal of Korean medical science · 2024Article
- The Present and Future of Artificial Intelligence-Based Medical Image in Diabetes Mellitus: Focus on Analytical Methods and Limitations of Clinical Use.Journal of Korean medical science · 2023Review
- Improving Machine Learning Diabetes Prediction Models for the Utmost Clinical Effectiveness.Journal of personalized medicine · 2022Article
- Retrospective cohort analysis comparing changes in blood glucose level and body composition according to changes in thyroid-stimulating hormone level.Journal of diabetes · 2022Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThere are many models for predicting diabetes mellitus (DM), but their clinical implication remains vague. Therefore, we aimed to create various DM prediction models using easily accessible health screening test parameters.
methodsTwo sets of variables were used to develop eight DM prediction models. One set comprised 62 easily accessible examination results of commonly used variables from a tertiary university hospital. The second set comprised 27 of the 62 variables included in the national routine health checkups. Gradient boosting and random forest algorithms were used to develop the models. Internal validation was performed using the stratified 10-fold cross-validation method.
resultsThe area under the receiver operating characteristic curve (ROC-AUC) for the 62-variable DM model making 12-month predictions for subjects without diabetes was the largest (0.928) among those of the eight DM prediction models. The ROC-AUC dropped by more than 0.04 when training with the simplified 27-variable set but still showed fairly good performance with ROC-AUCs between 0.842 and 0.880. The accuracy was up to 11.5% higher (from 0.807 to 0.714) when fasting glucose was included.
conclusionWe created easily applicable diabetes prediction models that deliver good performance using parameters commonly assessed during tertiary university hospital and national routine health checkups. We plan to perform prospective external validation, hoping that the developed DM prediction models will be widely used in clinical practice.
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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.