Habtamu Setegn Ngusie ^1* Getanew Aschalew Tesfa ² Asefa Adimasu Taddese ^3,4 Ermias Bekele Enyew ⁵ Tilahun Dessie Alene ⁶ Gebremeskel Kibret Abebe ⁷ Agmasie Damtew Walle ⁸ Alemu Birara Zemariam ⁹

• ¹ Department of Health Informatics, School of Public Health, College of Medicine and Health Sciences, Woldia University, Woldia, Ethiopia, Woldia, Ethiopia
• ² School of Public Health, College of Health and Medical Science, Dilla University, Dilla, SNNPR, Ethiopia
• ³ Department of Health Informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Amhara Region, Ethiopia
• ⁴ eHealthlab Ethiopia Research Center, University of Gondar, Gondar, Amhara Region, Ethiopia
• ⁵ Department of Health Informatics, School of Public Health, College of Medicine and Health Science, Wollo University, Dessie, Ethiopia
• ⁶ Department of Pediatric and Child health, School of Medicine, College of Medicine and Health Science, Wollo University, Dessie, Ethiopia, Dessie, Ethiopia
• ⁷ Department of Nursing, College of Health Science, Woldia University, Woldia, Ethiopia
• ⁸ Department of Health Informatics, College of Medicine and Health Science, Debre Berhan University, Debre Berhan, Ethiopia, Debre Berhan, Ethiopia
• ⁹ Department of Pediatrics and Child Health Nursing, School of Nursing, College of Medicine and Health Science, Woldia University, Woldia, Ethiopia, Woldia, Ethiopia

Background: Sub-Saharan Africa faces high neonatal and maternal mortality rates due to limited access to skilled healthcare during delivery. This study aims to improve the classification of health facilities and home deliveries using advanced machine learning techniques and to explore factors influencing women’s choices of delivery locations in East Africa. Method: The study focused on 86,009 childbearing women in East Africa. A comparative analysis of twelve advanced machine learning algorithms was conducted, utilizing various data balancing techniques and hyperparameter optimization methods to enhance model performance. Result: The prevalence of health facility delivery in East Africa was found to be 83.71%. The findings showed that the support vector machine (SVM) algorithm and CatBoost performed best in predicting the place of delivery, in which both of those algorithms scored an accuracy of 95% and an AUC of 0.98 after optimized with Bayesian optimization tuning and insignificant difference between them in all comprehensive analysis of metrics performance. Factors associated with facility-based deliveries were identified using association rule mining, including parental education levels, timing of initial antenatal care (ANC) check-ups, wealth status, marital status, mobile phone ownership, religious affiliation, media accessibility, and birth order. Conclusion: This study underscores the vital role of machine learning algorithms in predicting health facility deliveries. A slight decline in facility deliveries from previous reports highlights the urgent need for targeted interventions to meet Sustainable Development Goals (SDGs), particularly in maternal health. The study recommends promoting facility-based deliveries. These include raising awareness about skilled birth attendance, encouraging early ANC check-up, addressing financial barriers through targeted support programs, implementing culturally sensitive interventions, utilizing media campaigns, and mobile health initiatives. Design specific interventions tailored to the birth order of the child, recognizing that mothers may have different informational needs depending on whether it is their first or subsequent delivery. Furthermore, we recommended researchers to explore a variety of techniques and validate findings using more recent data.