چکیده مقاله
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 2975 women, 35 65 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 65 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 B12, 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
کلیدواژهها
نویسندگان
Amin Mansoori
Department of Applied Mathematics, Ferdowsi University of Mashhad, Mashhad, Iran
Habibollah Esmaily
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
شیوه ارجاع
Mansoori, Amin and Esmaily, Habibollah and Ghayour Mobarhan, Majid,1403,Predicting Metabolic Syndrome Based on Nutrient Intakes in Iranian Women Using a Decision Tree Data-mining Approach,1st International Biennial Conference of Artificial Intelligence and Data Science 2024,Bushehr
ارائهشده در
مجموعه مقالات اولین کنفرانس بین المللی دوسالانه هوش مصنوعی و علوم داده3 اردیبهشت 1403 · بوشهر