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

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.