Can machine learning predict BMI in early childhood using data from their first 1000 days of life? SciReports IUMedSchool AI artificialintelligence machinelearning ML bodymassindex BMI childhoodobesity childhealth
By Pooja Toshniwal PahariaJun 2 2023Reviewed by Lily Ramsey, LLM In a recent study published in the Scientific Reports Journal, researchers used a machine learning -based approach to predict adulthood obesity by assessing risk factors and tracking body mass index values in the initial 1,000 days of life.
After establishment, obesity is difficult to treat and likely to persist. Therefore, research prioritizes obesity prevention, and detecting individuals at a heightened risk of adiposity during adulthood could improve prevention efforts. About the study In the present study, researchers used ML algorithms to identify children at an increased risk of obesity, which could inform obesity prevention policymaking and strategy development. They also devised a dynamic, predictive BMI tracker to be used during childhood to identify the risk of adulthood obesity.
The steps involved in model development were obtaining and integrating raw data, pre-processing data, feature engineering, training, and tracker validation. The tracker was trained using 80.0% of individuals’ data from all periods. Eliminating erroneous records, imputing missing values, and scaling exposure variables, 50 variables were selected. After LASSO regression, data augmentation, and univariate tests, 19 variables were analyzed.
Factors such as the proportion of individuals residing in food deserts and Hispanic ethnicity protected against elevated BMI.
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