Application of machine learning algorithms to model predictors of informed contraceptive choice among reproductive age women in six high fertility rate sub Sahara Africa countries

Nearly six out of ten women received informed contraceptive choice, the magnitude is highest in Burkina Faso and lowest in Mali. Moreover, the highest spatial clustering of informed choice of contraceptive was observed in Burkina Faso while the lowest clustering was found in Angola. The LGBM classifier outperformed among machine learning algorithms and achieved 73% accuracy and an AUC of 0.80. Key factors influencing informed contraceptive choice were health facility visits, religion, contraceptive source, family planning messages, mobile ownership, education, wealth, residence, and lifetime partners. To enhance informed contraceptive choice, governments and policymakers should strengthen family planning education, expand healthcare services, and ensure equitable access to contraceptive information. Digital health solutions, especially mobile-based platforms, can also bridge information gaps. Integrating counseling into routine healthcare, training providers, and expanding mass media campaigns can enhance awareness. Engaging communities can help overcome social and religious barriers. Continuous monitoring and data-driven policy adjustments are essential for responsive interventions that address the evolving reproductive health needs in sub-Saharan Africa. Finally, we recommend that future research validate these findings using external data sources.