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Frontiers in Public Health|August 8, 2025
Machine learning algorithms to predict feeding practices during diarrheal disease and its determinants among under-five children in East AfricaTirualem Zeleke Yehuala, Nebebe Demis Baykemagn, Bewuketu Terefe
Frontiers in Global Women'S Health|February 25, 2025
Predicting pregnancy loss and its determinants among reproductive-aged women using supervised machine learning algorithms in Sub-Saharan AfricaTirualem Zeleke Yehuala, Sara Beyene Mengesha, Nebebe Demis Baykemagn
Digital Health|September 4, 2024
Acceptance of mobile application-based clinical guidelines among health professionals in Northwestern Ethiopia: A mixed-methods studyNebebe Demis Baykemagn, Araya Mesfin Nigatu, Berhanu Fikadie, et al.
BMC Health Services Research|November 23, 2024
Intention to use mobile text message reminders for medication adherence among hypertensive patients in North West Ethiopia: a cross-sectional studyEhite Melaku Zewdu, Adina Demessie, Araya Mesfin Nigatu, et al.
Infectious Diseases of Poverty|July 12, 2026
An ensemble machine learning approach for predicting anemia among under-five children in malaria-endemic sub-Saharan African countriesBerhan Tekeba, Nebebe Demis Baykemagn, Alexander Takele Mengesha, et al.
Scientific Reports|July 9, 2025
Exploring explainable machine learning algorithms to model predictors of tobacco use among men in Sub Sahara Africa between 2018 and 2023Mequannent Sharew Melaku, Nebebe Demis Baykemagn, Lamrot Yohannes, et al.
Digital Health|October 9, 2024
District health information system 2 data utilization among health professionals in Amara region private hospitals, EthiopiaAbraraw Gebre Mesele, Abreham Yeneneh Birhanu, Atsede Mazengia Shiferaw, et al.
BMC Public Health|January 17, 2025
Intention to use mobile phone-based TB screening among HIV patients in Debre Tabor Town public health facilities, Northwest Ethiopia: a cross-sectional studyTeshager Workneh Ayalew, Kassahun Dessie Gashu, Adamu Takele Jemere, et al.
Scientific Reports|August 8, 2025
Application of causal forest double machine learning (DML) approach to assess tuberculosis preventive therapy's impact on ART adherenceAbraham Keffale Mengistu, Kelemua Aschale Yeneakale, Nebebe Demis Baykemagn, et al.
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