Predicting Off-Track Development in Infants Aged 0 to 6 Months in Low-Resource Settings Using Machine Learning
Abstract
This study used machine learning models to predict developmental delays among infants aged 0–6 months in Kilifi, Kenya. Using data from 1,995 infants, the models showed good performance and identified limited psychosocial stimulation and increasing age as key predictors. The findings highlight the potential of machine learning for early prediction and targeted interventions in low-resource settings.
Fields of Study & Subjects
Child Nutrition and Water Access
Infant Development and Preterm Care
Early Childhood Education and Development
Publication details
- Journal / source
- OSF Preprints (OSF Preprints)
- Publication date
- 1 January 2026
- Language
- EN
- Document type
- Preprint