Identifying influential determinants of women's empowerment in Bangladesh using machine learning algorithms

Abstract

Background and objectives Women’s empowerment is a vital issue in lower-middle-income developing countries like Bangladesh, where it plays a pivotal role in advancing development across the nation. Thus, this study aimed to identify the influential determinants of women’s empowerment in Bangladesh using machine learning (ML) algorithms. Materials and methods The data for this study were obtained from the Bangladesh Demographic and Health Survey (BDHS) 2022, which included a nationally representative sample of 18,600 ever-married women aged 15–49 years. The important variables for women’s empowerment were identified using logistic regression and the Boruta feature selection method. Subsequently, eight popular machine learning algorithms - Decision Tree, Random Forest (RF), Naïve Bayes, Artificial Neural Network, Logistic Regression, Extreme Gradient Boosting, Gradient Boosting, and Support Vector Machine - were employed to predict women’s empowerment status. Model performance was assessed using accuracy, F1-score, and the area under the curve (AUC). Additionally, the most suitable model with SHAP analysis was used to identify the influential determinants driving women’s empowerment. Results The RF-based model demonstrated the best performance, achieving an accuracy of 71.07%, an F1-score of 81.58%, and an AUC of 0.676. The analysis revealed age, division, wealth index, working status, household members, husband’s education, and respondent’s education as the most influential determinants of women’s empowerment. Conclusion This study provides the best predictive model and identifies influential determinants of women’s empowerment in Bangladesh, offering valuable insights for achieving Sustainable Development Goal 5 (SDG-5) by 2030 through targeted actions and policies.