ArticleJournal of clinical laboratory analysis2023
Data mining approaches for type 2 diabetes mellitus prediction using anthropometric measurements.
Article in Journal of clinical laboratory analysis, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.
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Who cites it
31 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Malnutrition and type 2 diabetes mellitus across diverse global populations: a systematic review of observational evidence.Frontiers in clinical diabetes and healthcare · 2026Pooled it
- 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 Pilot Evaluation of a Wearable 12-Lead ECG System for Multilead Feature Analysis in Individuals with Different Glycemic Status.Sensors (Basel, Switzerland) · 2026Article
- Philippine Clinical Practice Guidelines for Periodic Health Examination: Screening for Renal, Metabolic, Nutritional, and Endocrine Disorders.Acta medica Philippina · 2026Article
- Physiological anthropometric associations with health metrics across different blood types among nursing students.Pakistan journal of medical sciences · 2025Article
- Serum vitamin D and lipid profile differences between breastfed and Formula-fed infants.Journal of health, population, and nutrition · 2025Article
- Article
- Predictive value of anthropometric indices for incident of dyslipidemia: a large population-based study.Population health metrics · 2025Article
- A machine learning-based framework for predicting metabolic syndrome using serum liver function tests and high-sensitivity C-reactive protein.Scientific reports · 2025Article
- Article
- 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
- Using a robust model to detect the association between anthropometric factors and T2DM: machine learning approaches.BMC medical informatics and decision making · 2025Article
- Blood indices of inflammation and their association with hypertension in smokers: analysis using data mining approaches.Journal of human hypertension · 2025Article
- Predictive properties of novel anthropometric and biochemical indexes for prediction of cardiovascular risk.Diabetology & metabolic syndrome · 2024Article
- Evaluating the Association of Anthropometric Indices With Total Cholesterol in a Large Population Using Data Mining Algorithms.Journal of clinical laboratory analysis · 2024Article
- Association of Obesity and Type 2 Diabetes Mellitus With Periodontitis: A Cross-Sectional Study.Cureus · 2024Article
- Analyzing Diabetes Detection and Classification: A Bibliometric Review (2000-2023).Sensors (Basel, Switzerland) · 2024Review
- Uric acid is associated with type 2 diabetes: data mining approaches.Diabetology international · 2024Article
- The associations of oxidative stress and inflammatory markers with obesity in Iranian population: MASHAD cohort study.BMC endocrine disorders · 2024Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe aim of this study was to evaluate the anthropometric measurements most associated with type 2 diabetes mellitus (T2DM) using machine learning approaches.
methodsA prospective study was designed for a total population of 9354 (43% men and 57% women) aged 35-65. Anthropometric measurements include weight, height, demispan, Hip Circumference (HC), Mid-arm Circumference (MAC), Waist Circumference (WC), Body Roundness Index (BRI), Body Adiposity Index (BAI), A Body Shape Index (ABSI), Body Mass Index (BMI), Waist-to-height Ratio (WHtR), and Waist-to-hip Ratio (WHR) were completed for all participants. The association was assessed using logistic regression (LR) and decision tree (DT) analysis. Receiver operating characteristic (ROC) curve was performed to evaluate the DT's accuracy, sensitivity, and specificity using R software.
resultsTraditionally, 1461 women and 875 men with T2DM (T2DM group). According to the LR, in males, WC and BIA (p-value < 0.001) and in females, demispan and WC (p-value < 0.001) had the highest correlation with T2DM development risk. The DT indicated that WC has the most crucial effect on T2DM development risk, followed by HC, and BAI.
conclusionsOur results showed that in both men and women, WC was the most important anthropometric factor to predict T2DM.
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