Evidence map›Paper›PMID 40854164›Full record

ArticleOnline journal of public health informatics2025

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

Shimels Derso Kebede

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Article in Online journal of public health informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1 citing paper in PubMed.

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Shimels Derso KebedeDepartment of Health Informatics, School of Public Health, College of Medicine and Health Science, Wollo University, South Wollo Zone, Amhara Region, Dessie, Ethiopia, 251 940219818.ORCID http://orcid.org/0000-0001-5416-616X

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6 · The paper itself

Abstract

Background: Neonatal disease and its outcomes are important indicators for a responsive health care system and encompass the effects of socioeconomic and environmental factors on new-borns and mothers. Ethiopia is working to achieve the Sustainable Development Goal target for the reduction of 12 or less per 1000 birth by 2030 and 21 per 1000 livebirths by 2025 as part of the second Ethiopian Health Sector Transformation Plan. Objective: This study aimed to compare the performance of classical time-series models with that of deep learning models and to forecast the neonatal mortality rate in Ethiopia to verify whether Ethiopia will achieve national and international targets. Methods: Data were extracted from the official World Bank database. Classical time-series models, such as autoregressive integrated moving average (ARIMA) and double exponential smoothing, and neural network-based models, such as multilayer perceptron, convolutional neural network, and long short-term memory, have been applied to forecast neonatal mortality rates from 2021 to 2030 in Ethiopia. During model building, the first 21 years of data (from 1990 to 2010) were used for training, and the remaining 10 years of data were used to test model performance. Model performance was evaluated using R², mean absolute percentage error (MAPE), and root mean squared error (RMSE). Finally, the best model was used to forecast the neonatal mortality rate over the next 10 years from 2021 to 2030, with a 95% prediction interval (PI). Results: The results showed that the double exponential smoothing model was the best, with a maximum R2 of 99.94% and minimum MAPE and RMSE of 0.002 and 0.0748, respectively. The worst performing among the 5 models was the CNN, with an R2 of 93.71% and a maximum RMSE of 0.79. Neonatal mortality in Ethiopia is forecasted to be 23.20 (PI 22.20-24.40) per 1000 live births in 2025 and 19.80 (PI 17.10-22.80) per 1000 live births in 2030. Conclusions: This study revealed that national and international targets for neonatal mortality cannot be realized if the current trend continues. This highlights the need for urgent interventions to strengthen the health system to fasten the decline rate of neonatal mortality and collaborative effort with concerned stakeholders for improved and responsive neonatal and child health services in order to achieve these targets.

Indexed as

deep learningdeep learning modelEthiopiaforecastinghealth sector transformation planmachine learningneonatal mortalitytime series

Identifiers

PMID40854164
PMCPMC12377635

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