Machine learning based prediction of low birth weight and its associated risk factors: Insights from the Bangladesh Demographic and Health Survey 2022.

ABSTRACT: Low birth weight (LBW) is a major public health concern particularly in low and middle-income countries as it contributes to increased infant mortality and long-term health complications. This study applies and evaluates machine learning (ML) algorithms to predict LBW and identify its key risk factors in Bangladesh. Data were collected from 3,192 complete records of ever-married women aged 15-49 years from the Bangladesh Demographic and Health Survey, 2022. Risk factors for LBW were identified by four feature selection techniques including Boruta-based selection (BFS), LASSO regression, Elastic Net and Random Forest (RF). Six ML algorithms, including Logistic Regression (LR), RF, Decision Tree (DT), Artificial Neural Networks (ANN), Extreme Gradient Boosting (XGB), and Light Gradient Boost

SUBMITTER: Sultana N 

PROVIDER: S-EPMC12483264 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature