Document Type
Article
Department
Institute for Human Development; Population Health (East Africa)
Abstract
Background This study aimed to address the critical gap in the limited application of machine learning (ML) for identifying developmental delays in low-resource settings by developing models to predict off-track development in infants aged 0 to 6 months and identify key predictors.
Methods A cross-sectional study involving 1,995 singleton infants aged 0 to 6 months was conducted in Kaloleni and Rabai sub-counties, Kilifi, Kenya, between March 2023 and March 2024. Development was assessed using the World Health Organization’s Indicators of Infant and Young Child Development tool, with Development-for-Age Z-scores used to classify infants as on- or off-track. Ridge logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost) models were trained using sociodemographic, psychosocial, clinical/biological, nutritional, and health-related predictors. Performance was evaluated using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. SHapley Additive exPlanations enhanced model interpretability.
Results Approximately 10.4% of infants were developmentally off-track. Ridge LR, RF, and XGBoost showed similar performance, with AUCs of 76.6%, 75.8%, and 76.1%, respectively. Limited psychosocial stimulation and increasing infant age were the strongest predictors.
Conclusions This study highlights the burden of developmental delays in low-resource settings. ML models
show promise for early risk prediction and targeted intervention, though further validation is
recommended.
Publication (Name of Journal)
Pediatric Research
DOI
https://doi.org/10.1038/s41390-026-04761-7
Recommended Citation
Benson, F.,
Odhiambo, R.,
Ngugi, A.,
Brink, W.,
Waljee, A.,
Moyer, C.,
Zhu, J.,
Agoi, F.,
Abubakar, A.
(2026). Predicting off-track development in infants aged 0–6 months in low-resource settings using machine learning. Pediatric Research, 1-9.
Available at:
https://ecommons.aku.edu/eastafrica_ihd/251
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.