Eliyas Addisu (MPH)
Research Fellow
Eliyas Addisu is a Research Fellow at the Center for Digital Health Implementation Science (CDHi), University of Gondar, and a passionate health informatics professional. He holds a Bachelor of Science in Health Informatics and a Master of Public Health in Health Informatics (CGPA: 3.98).During his undergraduate studies, Eliyas developed a “Web-Based Emergency Patient Information and Decision Support System” as his graduating project, highlighting his innovative approach to improving healthcare systems through technology. His MPH thesis focused on “predicting postpartum contraceptive utilization and identifying its determinate among reproductive-age women in 26 Sub-Saharan African countries,” showcasing his ability to apply advanced machine learning techniques to address public health challenges.
As part of his student journey, he worked as a secretary and Food Affairs at the University of Gondar Student Union from October 2020 to September 2022. Eliyas was the founder and producer of Chiret (“ጭረት”) Magazine, and he was a member of a team that translated “Scrum Guide” into Amharic.
He holds certifications in Applied Machine Learning (University of Michigan), AI for Medical Diagnosis (DeepLearning.AI), and Ethics and Governance of AI for Health (WHO). With expertise in machine learning, natural language processing, and spatial analysis, Eliyas is dedicated to advancing healthcare outcomes through innovative, data-driven solutions.
- Application of the random forest algorithm to predict skilled birth attendance and identify determinants among reproductive-age women in 27 Sub-Saharan African countries; machine learning analysis.
Available at: https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-025-22007-9
2. Random forest algorithm for predicting tobacco use and identifying determinants among pregnant women in 26 sub-Saharan African countries: a 2024 analysis.
Available at: https://doi.org/10.1186/s12889-025-22794-1
3. Machine learning algorithms to predict khat chewing practice and its predictors among men aged 15 to 59 in Ethiopia: further analysis of the 2011 and 2016 Ethiopian Demographic and Health Survey.
Available at: https://doi.org/10.3389/fpubh.2025.1555697
4. Predicting home delivery and identifying its determinants among women aged 15–49 years in sub-Saharan African countries using a Demographic and Health Surveys 2016–2023: a machine learning algorithm.
Available at: https://doi.org/10.1186/s12889-025-21334-1
5. 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.
Available at : https://rdcu.be/eoEkD
6. Cervical cancer screening uptake and its associated factor in Sub-Sharan Africa: a machine learning approach.
Available at : https://doi.org/10.1186/s12911-025-03039-y
7. Treatment satisfaction and associated factors among patients with rheumatoid arthritis in North West Ethiopia.
Available at : https://doi.org/10.1038/s41598-025-02199-1