ArticleJournal of education and health promotion2026
Demographics and obesity using a machine learning approach: Iranian National Obesity Registry (IRNOR).
Article in Journal of education and health promotion, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundObesity and overweight represent significant global health problems. The prevalence of obesity is increasing rapidly in developing countries and is involved in the etiology of cardiovascular disease, diabetes mellitus, and several cancers. In this study, we aimed to evaluate the relationship between demographic factors and obesity using machine learning (ML) within the population identified by the IRanian National Obesity Registry (IRNOR). MATERIALS AND
methodThis population-based cross-sectional study was started in 2021. A total of 2324 overweight and obese individuals were registered in IRanian National Obesity Registry (IRNOR) until October of 2022. The demographic data and anthropometric measurements of all participants were recorded. Multivariable logistic regression was used to evaluate relationships between demographic factors and obesity. Support vector machine (SVM) was applied to show the classification of important factors.
resultsData for age, education, smoking, marital, job, medical history, physical activity, and body mass index (BMI) were recorded for 2324 individuals. Of these individuals, 71.4% were female. The mean age and BMI were 42.50 years and 32.63 kg/m
conclusionThe SVM model may be effective in detection of priorities and could be helpful for more planned investigations in this field.
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