Exploring Machine learning Algorithms to Identify Determinants of Risky Behavior among Pregnant Women 15–59 years in Eastern African countries using the Demographic and Health Survey data

Introduction Unhealthy consumption patterns of substances, sexual activity, and physical inactivity are key contributors to morbidity and mortality for pregnant women. However, there is a limited study on those risk behaviors and their determinants among pregnant women in East Africa. Therefore, this study aimed to determine risky behaviors and their determinants among pregnant women in East Africa by using data from the DHS using machine learning algorithms. Methods This study utilized DHS data from 2012–2022 in 12 East African countries. Data was analyzed using Python version 3.7 and R version 4.3.3 for data preprocessing, modeling, and statistical analysis. Model performance was evaluated using accuracy and Area Under the Curve (AUC). Finally, the SHAP was applied in Python to further explore and interpret the predictors of risky behaviors among pregnant women aged 15–59 years old. Results In this study, the Light Gradient Boosting Machine model achieved an accuracy of 95.88% and an AUC score of 0.991. The SHapley Additive exPlanations analysis revealed that pregnant women who lived in rural areas, women with poor wealth income, women with middle wealth income, women whose husbands had primary education, and women not exposed to media increased risky behavior. Whereas women who were employed, women’s utilized ANC services, and women aged 25–36 lower likelihood of risky behaviors. Conclusion The Light GBM was the best-performing model for identifying determinants of risky behaviors among pregnant women in Eastern African countries. Interventions should focus on promoting and strengthening women’s ANC accessibility, improving husbands’ education, expanding media use, and economic empowerment for women to reduce the burden of risky behaviors.