This paper is in the following e-collection/theme issue:

Artificial Intelligence, Machine Learning, and Natural Language Processing for Public Health (89) Applications of AI (294) Advanced Data Analytics in eHealth (212) Machine Learning (2089) Pediatrics (162) Descriptive Epidemiology and Population Size Estimates (141) Public Health Policy (99) mHealth in the Developing World/LMICs, Underserved Communities, and for Global Health (718)

Published on in Vol 17 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/66798, first published .
Forecasting Neonatal Mortality in Ethiopia to Assess Progress Toward National and International Reduction Targets Using Classical Techniques and Deep Learning: Time-Series Forecasting Study

Forecasting Neonatal Mortality in Ethiopia to Assess Progress Toward National and International Reduction Targets Using Classical Techniques and Deep Learning: Time-Series Forecasting Study

Forecasting Neonatal Mortality in Ethiopia to Assess Progress Toward National and International Reduction Targets Using Classical Techniques and Deep Learning: Time-Series Forecasting Study

Authors of this article:

Shimels Derso Kebede1 Author Orcid Image
Article Authors Cited by Tweetations (1) Metrics

Shimels Derso Kebede   1 , MPH

1 Department of Health Informatics, School of Public Health, College of Medicine and Health Science, Wollo University, Dessie, Ethiopia

Corresponding Author:

  • Shimels Derso Kebede, MPH
  • Department of Health Informatics
  • School of Public Health, College of Medicine and Health Science, Wollo University
  • South Wollo Zone, Amhara Region
  • Dessie
  • Ethiopia
  • Phone: 251 940219818
  • Email: shime4d@gmail.com