Predicting Metabolic Syndrome Based on Nutrient Intakes in Iranian Women Using a Decision Tree Data-mining Approach

سال انتشار: 1403
محل انتشار: اولین کنفرانس بین المللی دوسالانه هوش مصنوعی و علوم داده
کد COI اختصاصی: DSAI01_055
زبان مقاله: انگلیسی
تعداد مشاهده: 173

نویسندگان

Department of Applied Mathematics, Ferdowsi University of Mashhad, Mashhad, Iran

Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran- Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran

Majid Ghayour Mobarhan

International UNESCO center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran

چکیده

Background and aims: The increasing incidence of metabolic syndrome (MetS) has become a major public health concern globally. Nutrients and dietary patterns are influential factors associated with the incidence of MetS. The main purpose of this study was to apply machine learning approaches to predict MetS based on micronutrients and macronutrients intakes in adult females from Mashhad, northeast of Iran.Method: This cross-sectional study was carried out on ۲۹۷۵ women, ۳۵-۶۵ years old, who participated in the MASHAD cohort study. MetS was defined according to the International Diabetes Federation (IDF). Dietary intakes were measured using a ۶۵-items food frequency questionnaire. Logistic regression (LR) and decision tree (DT) algorithms examined the associations between micro/macronutrients intakes and the risk of MetS.Results: According to the LR model, calcium, phosphate, potassium, vitamin B۱۲, thiamine, selenium, magnesium, and sodium were significantly related micronutrients associated with an increased prevalence of MetS. Fiber was the only macronutrients associated with MetS. According to the DT model, in micronutrients, magnesium was the most related factor related to the risk of MetS, followed by phosphate, potassium, sodium, and selenium. Fiber was the most important macronutrient associated with MetS.Conclusion: Magnesium, fiber, and calcium were the most essential nutrients in predicting MetS.

کلیدواژه ها

Data mining, Metabolic syndrome, Nutrients, Decision tree

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