Predicting rapid weight gain in six-month-old infants: an exploratory modeling study
Ana Daniela Ortega-Ramírez1, Carmen Alicia Sánchez-Ramírez1, Benjamín Trujillo-Hernández1
1Facultad de Medicina, Universidad de Colima, Colima, México.
Pediatric Research
|March 7, 2026
Summary
Predicting rapid weight gain (RWG) in infants is crucial for preventing childhood obesity. While statistical and machine-learning models showed modest results, identifying early predictors remains key for intervention strategies.
Area of Science:
- Pediatric Health
- Biostatistics
- Machine Learning in Healthcare
Background:
- Rapid weight gain (RWG) in early life is a significant risk factor for childhood obesity.
- The multifactorial etiology of RWG necessitates advanced analytical approaches for early prediction.
Purpose of the Study:
- To compare the predictive performance of machine-learning (SVM, Naïve Bayes) and statistical models (LASSO, GLM) for RWG from birth to 6 months.
- To identify key early-life predictors of RWG.
Main Methods:
- Prospective infant cohort data analyzed.
- Four predictive models evaluated: SVM (linear kernel), Naïve Bayes, LASSO regression, and Generalized Linear Model (GLM).
- Performance metrics included AUC, accuracy, precision, sensitivity, specificity, and F1-score with 95% CIs.
Main Results:
- All models demonstrated comparable precision (0.70-0.75).
- The GLM showed the highest point estimates for AUC (0.66), specificity (0.45), accuracy (0.72), and F1-score (0.800).
- LASSO achieved the highest sensitivity (0.91), but confidence intervals overlapped across all metrics, indicating no statistically significant differences between models.
Conclusions:
- All evaluated models exhibited similar, modest discriminative ability for predicting RWG.
- Consistent early-life predictors were identified, underscoring the multifactorial nature of RWG.
- Larger cohorts are required to enhance predictive accuracy and fully evaluate machine-learning model potential for RWG prediction.
