Research Article Open Access

Performance Evaluation of Machine Learning-Based Algorithms to Predict the Early Childhood Development Among Under Five Children in Bangladesh

Md. Ismail Hossain1, Iqramul Haq2, Ashis Talukder3,4, Sharmin Suraiya5, Mofasser Rahman6, Ahmed Abdus Saleh Saleheen1, Md. Injamul Haq Methun7, Md. Jakaria Habib1, Md. Sanwar Hossain1, Md. Iqbal Hossain Nayan5 and Sadiq Hussain8
  • 1 Department of Statistics, Jagannath University, Bangladesh
  • 2 Department of Agricultural Statistics, Sher-e-Bangla Agricultural University, Bangladesh
  • 3 Department of Statistics Discipline, Khulna University, Bangladesh
  • 4 National Centre for Epidemiology and Population Health, Australian National University, Canberra, ACT 2600, Australia
  • 5 Department of Pharmacy, Northern University, Bangladesh
  • 6 Department of Agribusiness and Marketing, Sher-e-Bangla Agricultural University, Bangladesh
  • 7 Department of Statistics Discipline, Tejgaon College, Bangladesh
  • 8 Examination Branch, Dibrugarh University, India


In this research, an effort has been made to apply a number of classifiers to predict Early Child Development (ECD) in the context of Bangladesh using the Bangladesh multiple indicator cluster survey, 2019 data set (i.e., to evaluate which sort of algorithm best identifies ECDI). To predict the ECD, nine well-known machine learning algorithms were applied, including Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), Least Absolute Shrinkage and Selection Operation (LASSO), Classification Trees (CT), AdaBoost and Neural Network (NN). Children aged 48-59 months who were female, attending early education, reading three or more children's books, having playthings, having normal nutritional status, and were not disabled had a higher percentage of completing at least three childhood development domains, according to the bivariate analysis results. We found several performance parameters for the classification of early childhood development, including the following: Accuracy (LR) = 67.87%, AUC (LR) = 67.49%; Accuracy (RF) = 67.23%, AUC (RF) = 67.19%; Accuracy (SVM) = 67.37%, AUC (SVM) = 67.64%; Accuracy (NB) = 67.55%, AUC (NB) = 66.80%; Accuracy (LASSO) = 68.04%, AUC (LASSO) = 67.75. Based on the results of this investigation, LASSO regression predicts the ECD in Bangladeshi children moderately better than any other machine learning method utilized in this study.

Journal of Computer Science
Volume 19 No. 5, 2023, 641-653


Submitted On: 19 October 2022 Published On: 5 May 2023

How to Cite: Hossain, M. I., Haq, I., Talukder, A., Suraiya, S., Rahman, M., Saleh Saleheen, A. A., Methun, M. I. H., Habib, M. J., Hossain, M. S., Nayan, M. I. H. & Hussain, S. (2023). Performance Evaluation of Machine Learning-Based Algorithms to Predict the Early Childhood Development Among Under Five Children in Bangladesh. Journal of Computer Science, 19(5), 641-653.

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  • Early Childhood Development
  • ML Algorithm
  • LASSO Regression
  • Bangladesh