ArticleScientific reports2023
Prediction of type 2 diabetes mellitus using hematological factors based on machine learning approaches: a cohort study analysis.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 3 of them syntheses that pooled it.
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Who cites it
36 citing papers in PubMed, 3 syntheses or guidelines pooled it, 78 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
- Changes in selected hematological parameters in patients with type 1 and type 2 diabetes: a systematic review and meta-analysis.Frontiers in medicine · 2024Pooled it
- Application of machine learning in measurement of ageing and geriatric diseases: a systematic review.BMC geriatrics · 2023Pooled it
- Trial
- A machine learning-based framework for predicting type 2 diabetes mellitus using hematological indices.American heart journal plus : cardiology research and practice · 2026Article
- Routine Haematological Parameters Associated with HbA1c and Estimated Whole-Blood Viscosity in Diabetes Management: An Exploratory AIC-Based Regression Analysis.Journal of clinical medicine · 2026Article
- Exploratory Machine Learning-Based Classification of Type 2 Diabetes Using Routine Clinical Parameters: A Single-Center Comparative Study.Healthcare (Basel, Switzerland) · 2026Article
- In newly diagnosed diabetes, hyperglycemia is associated with increased brain iron deposition, measured by MRI-based Quantitative Susceptibility Mapping (QSM).Journal of advanced research · 2026Article
- Multidimensional feature fusion in longitudinal physical examination data: a machine learning framework for classification of type 2 diabetes.Frontiers in public health · 2026Article
- Article
- Demographics and obesity using a machine learning approach: Iranian National Obesity Registry (IRNOR).Journal of education and health promotion · 2026Article
- Evolution of diabetes prediction using the fusion of ANOVA, ADASYN technique and XGBoost based on body composition data.Journal of diabetes and metabolic disorders · 2025Article
- Association between isolated minor ST-segment and T wave changes with 10 years all-cause and stroke mortality.Scientific reports · 2025Article
- Investigating the relationship between blood factors and HDL-C levels in the bloodstream using machine learning methods.Journal of health, population, and nutrition · 2025Article
- Article
- Patterns of type 2 diabetes risk factors using latent class analysis (LCA) model: a population-based study in the South of Iran, Kharameh cohort population.BMC public health · 2025Article
- A machine learning-based framework for predicting metabolic syndrome using serum liver function tests and high-sensitivity C-reactive protein.Scientific reports · 2025Article
- T2D-LVDD: neural network-based predictive models for left ventricular diastolic dysfunction in type 2 diabetes.Diabetology & metabolic syndrome · 2025Article
- Nutritional intake of micronutrient and macronutrient and type 2 diabetes: machine learning schemes.Journal of health, population, and nutrition · 2025Article
- A novel index for diagnosis of type 2 diabetes mellitus: Cholesterol, High density lipoprotein, and Glucose (CHG) index.Journal of diabetes investigation · 2025Article
Corrections and comments
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Authors and funding
13 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Type 2 Diabetes Mellitus (T2DM) is a significant public health problem globally. The diagnosis and management of diabetes are critical to reduce the diabetes complications including cardiovascular disease and cancer. This study was designed to assess the potential association between T2DM and routinely measured hematological parameters. This study was a subsample of 9000 adults aged 35-65 years recruited as part of Mashhad stroke and heart atherosclerotic disorder (MASHAD) cohort study. Machine learning techniques including logistic regression (LR), decision tree (DT) and bootstrap forest (BF) algorithms were applied to analyze data. All data analyses were performed using SPSS version 22 and SAS JMP Pro version 13 at a significant level of 0.05. Based on the performance indices, the BF model gave high accuracy, precision, specificity, and AUC. Previous studies suggested the positive relationship of triglyceride-glucose (TyG) index with T2DM, so we considered the association of TyG index with hematological factors. We found this association was aligned with their results regarding T2DM, except MCHC. The most effective factors in the BF model were age and WBC (white blood cell). The BF model represented a better performance to predict T2DM. Our model provides valuable information to predict T2DM like age and WBC.
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Registered trials
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.