Analyzing and forecasting under-5 mortality trends in Bangladesh using machine learning techniques.

Journal: PloS one
PMID: 39919148

Abstract

BACKGROUND: Under-5 mortality remains a critical social indicator of a country's development and economic sustainability, particularly in developing nations like Bangladesh. This study employs machine learning models, including Linear Regression, Ridge Regression, Lasso Regression, Bayesian Ridge, Decision Tree, Gradient Boosting, XGBoost, and CatBoost, to forecast future trends in under-5 mortality. By leveraging these models, the study aims to provide actionable insights for policymakers and health professionals to address persistent challenges.

Authors

  • Shayla Naznin
    Department of Statistics, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh.
  • Md Jamal Uddin
    ABEx Bio-Research Center, East Azampur, Dhaka, Bangladesh.
  • Ishmam Ahmad
    FCPS (Internal Medicine) Part-II Trainee, Medicine Unit-2, Shaheed Suhrawardy Medical College Hospital, Dhaka, Bangladesh.
  • Ahmad Kabir
    Department of Statistics, Shahjalal University of Science and Technology, Sylhet, Bangladesh.

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