Predicting off-track development in infants aged 0–6 months in low-resource settings using machine learning

Abstract

Early intervention programs are proven to enhance optimal childhood development, yet the vital step of early identification of developmental delays is often overlooked. While machine learning is increasingly used to predict or identify health outcomes, its application in identifying developmental outcomes, particularly in low-resource settings, remains limited. This study contributes to the literature by applying machine learning to identify infants who are developmentally off-track and highlights key predictors. Limited psychosocial stimulation and increasing infant age were the strongest predictors, alongside low socioeconomic status, maternal mental health challenges, limited healthcare access, and nutritional and biological risks.