Abstract
Backgrounds: Compelling evidence shows that rapid weight gain(RWG) during infancy is highly predictive of subsequent obesity. Infant RWG is a useful proxy marker denoting future obesity risk that could facilitate obesity risk prediction from infancy. Leveraging novel machine learning approaches, this study aims to develop and validate risk prediction models to identify RWG by age one year using prenatal and early postnatal factors.
Methods: Pooled data from seven Australian and New Zealand cohorts (n=5233) were used. Predictors included maternal pre-pregnancy BMI, smoking status, gestational age, number of siblings, infant sex, birth weight, breastfeeding duration, and timing of solid introduction. Eight machine learning algorithms were used to predict infant RWG, which was defined as a change in weight-for-age z-score ≥ 0.67 from birth to around age one year. Pooled data were randomly split into a training and a test dataset for model training and validation respectively. Model consistency was evaluated using five-fold cross-validation. Model predictive performance was evaluated by Area Under the Curve (AUC), accuracy, precision, sensitivity, and specificity.
Results: Average prevalence of infant RWG was 27%. All machine learning methods showed acceptable to excellent discrimination with AUC ranging from 0.75 to 0.85. Accuracy, which indicates the overall correctness of the model, spanned from 0.70 to 0.78. Precision, that measures the model's ability to avoid false positives, ranged from 0.70 to 0.77. The range of sensitivity(true positive cases) and specificity(true negative cases) of all models was 0.68 - 0.80 and 0.65 - 0.78, respectively. Random Forest and Gradient Boosting Classifier algorithms showed the most favourable predictive accuracy.
Conclusion: Utilising prenatal and early postnatal factors, the resulting machine learning based prediction models showed acceptable to excellent ability to identify and distinguish between infants with or without RWG. Such models could be feasibly integrated in clinical practice for early obesity risk assessment.