Unveiling socio-demographic determinants of low birth weight using machine learning techniques
Mohammad Safi Uddin, Md. Refath Islam, K. M. Ariful Kabir
TL;DR
This study uses machine learning to identify key factors causing low birth weight in Bangladesh and develops a predictive model to help identify at-risk mothers.
Contribution
The study introduces a machine learning-based predictive model for low birth weight using socio-demographic factors specific to Bangladesh.
Findings
Age at first birth and education level are the most influential predictors of low birth weight.
AdaBoost achieved the highest predictive accuracy among tested machine learning models.
Abstract
Low birth weight (LBW) poses significant challenges to child survival, contributing to increased rates of mortality and morbidity, and has long-term adverse effects on overall health. The persistently high prevalence of LBW in low- and middle-income countries, including Bangladesh, reflects underlying health disparities. Despite recent improvements, Bangladesh still reports a notable LBW rate of 14.5%, indicating persistent maternal and child health concerns. Various socio-demographic factors influence birth weight, necessitating a comprehensive investigation into their contributions. This study aims to identify the key determinants of LBW and develop a machine learning-based predictive model to assess vulnerable mothers of having LBW babies based on risk factors associated with birth weight. Data for this study were obtained from the Bangladesh Demographic and Health Survey (BDHS)…
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Taxonomy
TopicsGestational Diabetes Research and Management · Maternal and Neonatal Healthcare · Global Maternal and Child Health