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While the first 1000 days are demonstrably critical for obesity prevention, a significant gap exists in existing childhood obesity prediction models, which rarely encompass risk factors across the prenatal and early infancy phases. Machine learning was a key technique in this study to determine the impact of factors occurring within the first 1000 days on childhood body mass index (BMI). In a study of childhood obesity, LASSO regression identified 13 features relevant t
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