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Machine learning analysis for the association between breast feeding and metabolic syndrome in womenopen access

Authors
Lee, Jue SeongChoi, Eun-SaemLee, HwasunSon, SerhimLee, Kwang-SigAhn, Ki Hoon
Issue Date
Feb-2024
Publisher
Nature Publishing Group
Citation
Scientific Reports, v.14, no.1
Indexed
SCIE
SCOPUS
Journal Title
Scientific Reports
Volume
14
Number
1
URI
https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/65828
DOI
10.1038/s41598-024-53137-6
ISSN
2045-2322
Abstract
This cross-sectional study aimed to develop and validate population-based machine learning models for examining the association between breastfeeding and metabolic syndrome in women. The artificial neural network, the decision tree, logistic regression, the Naive Bayes, the random forest and the support vector machine were developed and validated to predict metabolic syndrome in women. Data came from 30,204 women, who aged 20 years or more and participated in the Korean National Health and Nutrition Examination Surveys 2010-2019. The dependent variable was metabolic syndrome. The 86 independent variables included demographic/socioeconomic determinants, cardiovascular disease, breastfeeding duration and other medical/obstetric information. The random forest had the best performance in terms of the area under the receiver-operating-characteristic curve, e.g., 90.7%. According to random forest variable importance, the top predictors of metabolic syndrome included body mass index (0.1032), medication for hypertension (0.0552), hypertension (0.0499), cardiovascular disease (0.0453), age (0.0437) and breastfeeding duration (0.0191). Breastfeeding duration is a major predictor of metabolic syndrome for women together with body mass index, diagnosis and medication for hypertension, cardiovascular disease and age.
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Ahn, Ki Hoon
Anam Hospital (Department of Obstetrics and Gynecology, Anam Hospital)
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