Application of machine learning algorithms to predict early childhood development in children aged 24-59 months across three East African countries.

ABSTRACT:

Background

Early childhood development (ECD) plays a crucial role in shaping the future development of children and it influences their lifelong outcomes. The Early childhood development index 2030 (ECDI2030) serves as an effective tool for monitoring the overall development of children aged 24-59 months at the population level. This study employed machine learning algorithms to identify the predictors of ECD across three East African countries, using the ECDI2030.

Methods

Data were derived from the Demographic and Health Surveys of Kenya, Mozambique, and Tanzania. Seven supervised machine learning algorithms and an ensemble of the best performing models were utilized to predict ECD. The dataset was randomly divided into 80% training and 20% testing sets. The predictive abil

SUBMITTER: Tebeje TM 

PROVIDER: S-EPMC12431311 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature