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Related Experiment Video

Updated: Jan 12, 2026

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
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Predicting Child Development Across Literacy, Physical, Learning, and Social-Emotional Domains Using Supervised

Faizul Islam1, Golam Morshed Suhel2, Mahmud Afroz3

  • 1Department of Statistics Shahjalal University of Science & Technology Sylhet Bangladesh.

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Summary

Early childhood development (ECD) is crucial, with machine learning identifying key predictors like maternal education and socioeconomic status. Interventions targeting literacy-numeracy and social-emotional skills are vital for optimal child well-being.

Keywords:
algorithmscognitionearly childhood developmentmachine learningpsychomotor performancesocial behaviorstatistical models

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Area of Science:

  • Child Development
  • Machine Learning Applications
  • Public Health

Background:

  • Early childhood development (ECD) is critical for lifelong health and well-being.
  • Early detection and intervention are essential to prevent long-term developmental impairments.
  • Factors influencing ECD include child, family, and environmental elements.

Purpose of the Study:

  • To predict ECD across four key domains using supervised machine learning.
  • To identify the most significant predictors of ECD.
  • To inform economic strategies for supporting child development.

Main Methods:

  • Utilized data from 9,346 children from the Multiple Indicator Cluster Surveys (MICS) 2019.
  • Compared five machine learning classifiers: CART, Random Forest, XGBoost, Logistic Regression, and SVM.
  • Employed five-fold cross-validation and Synthetic Minority Oversampling Technique (SMOTE) for data imbalance.

Main Results:

  • Most children showed normal development in learning (90.58%) and physical (98.70%) domains.
  • Highest delays observed in literacy-numeracy (71.37%) and social-emotional (27.57%) domains.
  • Random Forest model achieved the highest accuracy, with maternal education, child age, and socioeconomic status as key predictors.

Conclusions:

  • Significant disparities in ECD exist, especially in social-emotional and literacy-numeracy domains.
  • Maternal education, early learning experiences, and socioeconomic status are crucial predictors of ECD.
  • Findings support targeted interventions and policies for holistic child development.