I am Andualem Enyew Gedefaw, a researcher applying machine learning and deep learning to advance health data science. I focus on analyzing population health datasets to uncover patterns, identify key risk factors of infectious diseases and maternal and child health, and improve predictive modeling for better health outcomes. My work bridges digital health, health information systems (HIS), and data-driven public health insights.
Andualem Enyew Gedefaw is a Lecturer in the Department of Health Informatics at the University of Gondar, Ethiopia. He holds a Master of Science in Health Data Science and has extensive experience in data-driven health research, including predictive modeling, machine learning, deep learning, and the application of advanced analytics to public health data. His research interests include digital health, epidemiological modeling, maternal and child health, and the use of artificial intelligence to strengthen health systems. He is passionate about leveraging data science to inform evidence-based decision-making and improve healthcare outcomes.
digital health, epidemiological modeling, maternal and child health, and the use of artificial intelligence to strengthen health systems.
He is passionate about leveraging data science to inform evidence-based decision-making and improve healthcare outcomes.
Application of causal forest double machine learning (DML) approach to assess tuberculosis preventive therapy’s impact on ART adherence
Insights into long-acting reversible contraceptive practices in Sub-Saharan Africa: A machine learning perspective
Machine learning predicts prolonged patient length of stay in a resource constrained Ethiopian hospital
Machine learning to examine adequate awareness and positive perception of HIV pre-exposure prophylaxis among women in sub-Saharan Africa: evidence from 2021-2024 surveys
Predicting breast self-examination awareness in Sub-Saharan Africa using machine learning
Predicting Car Accident Severity in Northwest Ethiopia: A Machine Learning Approach Leveraging Driver, Environmental, and Road Conditions