Predicting Cervical Cancer Screening Utilization Among Women of Reproductive Age in Ethiopia Using Supervised Machine Learning: A Nationally Representative 2024/25 EDHS Study

Abstract Background: Cervical cancer is a leading malignancy and mortality cause among Ethiopian women. Despite screening expansions coverage remains low. Traditional models miss complex determinants. This study aimed to develop machine learning models to predict cervical cancer screening utilization and identify drivers using the 2024 to 2025 Ethiopian Demographic and Health Survey. Methods: A secondary analysis of the 2024 to 2025 Ethiopian Demographic and Health Survey was conducted among women aged 15 to 49. Six algorithms Logistic Regression, Decision Tree, Support Vector Machine, Random

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