ArticleDiagnostics (Basel, Switzerland)2022
Development and Validation of an Insulin Resistance Predicting Model Using a Machine-Learning Approach in a Population-Based Cohort in Korea.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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29 citing papers in PubMed, 38 citations in OpenAlex.
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- Assessment of Machine Learning Model Performance for Clinical Prediction of Insulin Resistance in the Study of Cardiovascular Risk in Adolescents-ERICA.Journal of clinical medicine · 2026Article
- Mini-review on insulin resistance assessment: Advances in surrogate indices and clinical applications.World journal of clinical cases · 2025Review
- Developing an interpretable machine learning model for easily detecting insulin resistance among breast cancer survivors: a cross-sectional study.BMC medical informatics and decision making · 2025Article
- AI-driven prediction of insulin resistance in non-diabetic populations using minimal invasive tests: comparing models and criteria.Diabetology & metabolic syndrome · 2025Article
- Prediction Model for Insulin Resistance and Implications for MASLD in Youth: A Novel Marker, the Pediatric Insulin Resistance Assessment Score.Yonsei medical journal · 2025Article
- Prediction of Insulin Resistance in Nondiabetic Population Using LightGBM and Cohort Validation of Its Clinical Value: Cross-Sectional and Retrospective Cohort Study.JMIR medical informatics · 2025Article
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- Association between the C-reactive protein-triglyceride-glucose index and endometriosis: a cross-sectional study using data from the national health and nutrition examination survey, 1996-2006.BMC women's health · 2025Article
- Extreme gradient boosting using conventional parameters accurately predicts insulin sensitivity in young and middle-aged Japanese persons.Frontiers in endocrinology · 2025Article
- Effects of Various Heavy Metal Exposures on Insulin Resistance in Non-diabetic Populations: Interpretability Analysis from Machine Learning Modeling Perspective.Biological trace element research · 2024Article
- Relationship between triglyceride-glucose index and endometriosis: a cross-sectional analysis.BMC women's health · 2024Article
- Validation of the Framingham Diabetes Risk Model Using Community-Based KoGES Data.Journal of Korean medical science · 2024Article
- Development and validation of machine learning-augmented algorithm for insulin sensitivity assessment in the community and primary care settings: a population-based study in China.Frontiers in endocrinology · 2024Article
- Discriminating insulin resistance in middle-aged nondiabetic women using machine learning approaches.AIMS public health · 2024Article
- Diabetes Mellitus in Pancreatic Cancer: A Distinct Approach to Older Subjects with New-Onset Diabetes Mellitus.Cancers · 2023Review
- Polygenic Variants Linked to Oxidative Stress and the Antioxidant System Are Associated with Type 2 Diabetes Risk and Interact with Lifestyle Factors.Antioxidants (Basel, Switzerland) · 2023Article
- Fecal Microbiota Composition, Their Interactions, and Metagenome Function in US Adults with Type 2 Diabetes According to Enterotypes.International journal of molecular sciences · 2023Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
backgroundInsulin resistance is a common etiology of metabolic syndrome, but receiver operating characteristic (ROC) curve analysis shows a weak association in Koreans. Using a machine learning (ML) approach, we aimed to generate the best model for predicting insulin resistance in Korean adults aged > 40 of the Ansan/Ansung cohort using a machine learning (ML) approach.
methodsThe demographic, anthropometric, biochemical, genetic, nutrient, and lifestyle variables of 8842 participants were included. The polygenetic risk scores (PRS) generated by a genome-wide association study were added to represent the genetic impact of insulin resistance. They were divided randomly into the training (
resultsThe cutoff of insulin resistance defined with HOMA-IR was 2.31 using logistic regression before conducting ML. XGBoost and logistic regression algorithms generated the highest AUC (0.86) of the prediction models using 99 features, while the random forest algorithm generated a model with 0.82 AUC. These models showed high accuracy and k-fold values (>0.85). The prediction model containing 15 features had the highest AUC of the ROC curve in XGBoost and random forest algorithms. PRS was one of 15 features. The final prediction models for insulin resistance were generated with the same nine features in the XGBoost (AUC = 0.86), random forest (AUC = 0.84), and artificial neural network (AUC = 0.86) algorithms. The model included the fasting serum glucose, ALT, total bilirubin, HDL concentrations, waist circumference, body fat, pulse, season to enroll in the study, and gender.
conclusionThe liver function, regular pulse checking, and seasonal variation in addition to metabolic syndrome components should be considered to predict insulin resistance in Koreans aged over 40 years.
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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.