Eliyas Addisu

Eliyas Addisu is a Digital Health Expert currently working at CDHi, with a Master of Public Health in Health Informatics. He has extensive experience in developing and implementing electronic medical record systems, including eCHIS, and excels as a system analyst and machine learning researcher. His expertise spans EMR software development, health data management, and applying machine learning models to complex healthcare datasets to inform decision- making. Eliyas has contributed to multiple digital health projects and research initiatives, producing over ten publications in machine learning and health informatics. His work bridges technology and public health, delivering innovative solutions that enhance healthcare delivery and support evidence-based interventions.

1. EMR

1. 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

8. Forecasting tuberculosis in Ethiopia using deep learning: progress toward sustainable development goal evidence from global burden of disease 1990–2021.

            Available at: https://rdcu.be/euq03

9. Predicting delayed antenatal care initiation among pregnant women in East Africa: using machine learning algorithms.

          Available at: https://doi.org/10.3389/fgwh.2025.1488391

10. Geographically weighted regression analysis of incomplete basic childhood vaccination in Sub-Saharan Africa: Evidence from DHS, 2019–2024

         Available at: https://doi.org/10.1371/journal.pone.0336498