Predicting body mass index in early childhood using data from the first 1000 days.
Sci Rep
; 13(1): 8781, 2023 05 31.
Article
em En
| MEDLINE
| ID: mdl-37258628
ABSTRACT
Few existing efforts to predict childhood obesity have included risk factors across the prenatal and early infancy periods, despite evidence that the first 1000 days is critical for obesity prevention. In this study, we employed machine learning techniques to understand the influence of factors in the first 1000 days on body mass index (BMI) values during childhood. We used LASSO regression to identify 13 features in addition to historical weight, height, and BMI that were relevant to childhood obesity. We then developed prediction models based on support vector regression with fivefold cross validation, estimating BMI for three time periods 30-36 (N = 4204), 36-42 (N = 4130), and 42-48 (N = 2880) months. Our models were developed using 80% of the patients from each period. When tested on the remaining 20% of the patients, the models predicted children's BMI with high accuracy (mean average error [standard deviation] = 0.96[0.02] at 30-36 months, 0.98 [0.03] at 36-42 months, and 1.00 [0.02] at 42-48 months) and can be used to support clinical and public health efforts focused on obesity prevention in early life.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Obesidade Infantil
Tipo de estudo:
Diagnostic_studies
/
Etiology_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Child
/
Child, preschool
/
Female
/
Humans
/
Pregnancy
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article