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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 Naïve 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. [ABSTRACT FROM AUTHOR]

We investigated the association between obesity and cardiovascular disease risk factors (CVDRFs) in adolescents. We performed a cross-sectional study using the data from 8149 adolescents, aged 10–18 years, included in the Korean National Health and Nutrition Examination Survey (2011–2020). Using the body mass index, we defined "overweight" (≥85th to <95th percentile) and "obese" (≥95th percentile). We analyzed the associations between obesity and CVDRFs (high blood pressure, abnormal lipid profiles, and high fasting glucose levels) by sex and age groups (early [10–12 years], middle [13–15 years], and late [16–18 years] adolescence). When analyzing all the subjects, being overweight was correlated with high blood pressure and abnormal all-lipid profiles in boys and high triglyceride and low high-density lipoprotein cholesterol levels in girls, while obesity was associated with all CVDRFs in both boys and girls. Analyzing separately in the age subgroups, the correlation between obesity and CVDRFs tended to be shown earlier in boys than in girls, and obesity tended to be associated with CVDRFs earlier than being overweight. The association between obesity and CVDRFs may begin to be shown at different periods of youth, depending on the degree of obesity, CVDRF variables, and sex. [ABSTRACT FROM AUTHOR]