Predicting Early Childhood Development in Bangladesh Using Hybrid Machine Learning Model and SHAP Explainability

Saiful Islam, Md. Palash Bin Faruque, Tanjina Khan, Most. Shabrina Afroz, Tofayel Ahmed, Md Muhtasim Munif Fahim, Md. Kamruzzaman, Md. Mostafizur Rahman, Md. Abdul Khalek

2026 IEEE 2nd International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN) · 2026 · published

Abstract

Early childhood cognitive and social-emotional development is crucial for shaping lifelong health, education, and productivity. However, many children in low- and middle-income countries don't achieve their growth potential. This study applies a hybrid machine learning framework with explainable artificial intelligence SHAP to predict early childhood cognitive and social-emotional development outcomes in Bangladesh and also identify the key contributing factors. Using nationally representative data from the Bangladesh Multiple Indicator Cluster Survey (MICS) 2019, a sample of 9,455 children aged 36–59 months was analyzed. Cognitive and social-emotional improvement were modeled as binary outcomes was described on the basis of individual classifiers (Support Vector Machine, Random Forest, XGBoost, and Logistic Regression) and a hybrid model that combined SVM, RF, and XGBoost. The model was measured using accuracy, precision, recall, F1-score, specificity, and ROC-AUC to evaluate the level of performance, and SHAP explainability was applied to enhance interpretability. Moreover, subgroup SHAP analysis was conducted to compare feature contributions for urban and rural children. The hybrid model achieved the best overall performance in both domains, with an accuracy of 0.77 and ROC-AUC of 0.79 for cognitive development, and an accuracy of 0.71 and ROC-AUC of 0.72 for social-emotional development. Access to children's books, the child's age, and signing up for early childhood education were the most important factors in cognitive growth. The social-emotional development was most strongly related with the contextual and caregiving factors, which included the geographic location, exposure to violent forms of discipline, and positive caregiver-child relationships. These results demonstrate that a hybrid model combined with SHAP explainability can be helpful to identify complex and domain-specific factors of early childhood development. SHAP subgroup analysis shows urban predictions rely on behavior/context, while rural predictions depend more on caregiving, resources, and nutrition. The approach provides a transparent, data-driven mechanism to facilitate evidence-based policymaking and targeted interventions in accordance with Sustainable Development Goal 4.2.

BibTeX

@article{islam2026predicting,
  title   = {Predicting Early Childhood Development in Bangladesh Using Hybrid Machine Learning Model and SHAP Explainability},
  author  = {Saiful Islam and Md. Palash Bin Faruque and Tanjina Khan and Most. Shabrina Afroz and Tofayel Ahmed and Md Muhtasim Munif Fahim and Md. Kamruzzaman and Md. Mostafizur Rahman and Md. Abdul Khalek},
  year    = {2026},
  journal = {2026 IEEE 2nd International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN)},
  doi     = {10.1109/QPAIN69676.2026.11545516},
}