Random forest algorithm for predicting tobacco use and identifying determinants among pregnant women in 26 sub-Saharan African countries: a 2024 analysis

This study utilized a Random Forest machine learning algorithm to identify key predictors of tobacco use among pregnant women across 26 Sub-Saharan African countries. Significant factors included maternal literacy, education, wealth index, and healthcare access, highlighting systemic inequities contributing to tobacco dependency during pregnancy. These findings advocate for policies addressing educational disparities, economic inequalities, and barriers to healthcare access to reduce tobacco use and improve maternal and neonatal outcomes. Future research should incorporate longitudinal data to enhance predictive accuracy and inform policy development.