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IMPACT OF FERTILITY ON UNDER-5 MORTALITY IN BANGLADESH USING MACHINE LEARNING: INSIGHTS FROM BDHS 2017-18
Keywords:
fertility, under-5 mortality, machine learning, XG Boost, birth spacing, BangladeshDOI:
https://doi.org/10.17654/0972361725019Abstract
This study examines the relationship between fertility and under-5 mortality in Bangladesh using machine learning techniques. Utilizing data from the Bangladesh Demographic and Health Survey (BDHS) 2017-18, the study explores how factors like birth intervals, maternal age and total children ever born influence under-5 mortality. By applying advanced models like XGBoost, Random Forest and Gradient Boosting, we uncover hidden patterns and non-linear effects that traditional methods often overlook offering more comprehensive insights into how fertility impacts under-5 mortality. These insights provide actionable recommendations for public health policy focused on fertility management to reduce under-5 mortality rates. Machine learning adds value by identifying patterns critical to child health interventions.
Received: October 4, 2024
Revised: November 5, 2024
Accepted: November 25, 2024
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