Evidence map›Paper›PMID 40036282›Full record

ArticlePloS one2025

Exploring the achievements and forecasting of SDG 3 using machine learning algorithms: Bangladesh perspective.

Md Maeen Molla, Md Sifat Hossain, Md Ayub Ali, Md Raqibul Islam, Mst Papia Sultana, Dulal Chandra Roy

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Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Md Maeen MollaDepartment of Statistics, Pirojpur Science and Technology University, Pirojpur, Bangladesh.ORCID https://orcid.org/0009-0000-9315-7338
Md Sifat HossainDepartment of Statistics, University of Rajshahi, Rajshahi, Bangladesh.ORCID https://orcid.org/0009-0000-7265-5143
Md Ayub AliDepartment of Statistics, University of Barishal, Barishal, Bangladesh.
Md Raqibul IslamDepartment of Statistics, University of Rajshahi, Rajshahi, Bangladesh.
Mst Papia SultanaDepartment of Statistics, University of Rajshahi, Rajshahi, Bangladesh.
Dulal Chandra RoyDepartment of Statistics, University of Rajshahi, Rajshahi, Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSustainable Development Goal 3 (SDG 3), focusing on ensuring healthy lives and well-being for all, holds global significance and is particularly vital for Bangladesh. Neonatal Mortality Rate (NMR), Under-5 Mortality Rate (U5MR), Maternal Mortality Ratio (MMR) and Death Rate Due to Road Traffic Injuries (RTI) are considered responsible indicators of SDG 3 progress in Bangladesh. The objective of the study is to forecast these indicators of Bangladesh up to 2030 and compare these forecasts with predetermined 2030 targets. The data is obtained from the World Bank's (WB) website.

methodFor forecasting, time series models were employed, specifically Autoregressive Integrated Moving Average- ARIMA (0,2,1) with Akaike Information Criterion (AIC) 94.6 for NMR and ARIMA (2,1,2) with AIC 423.2 for U5MR, selected based on their lowest AIC values. Additionally, Machine Learning (ML) models, including Bidirectional Recurrent Neural Networks (BRNN) and Elastic Neural Networks (ENET), were employed for all the indicators.

resultsENET demonstrates superior performance compared to both BRNN and ARIMA in the context of NMR, achieving a Root Mean Absolute Error (RMAE) of 0.603446 and a Root Mean Square Error (RMSE) of 0.451162. Furthermore, when considering U5MR, MMR, and Death Rate Due to RTI, ENET consistently exhibits lower error metrics compared to the alternative models. Following the time series and ML analyses, a consistent trend emerges in the forecasted values for NMR and U5MR, which consistently fall below their respective 2030 targets. This promising finding suggests that Bangladesh is making significant progress toward meeting its 2030 targets for NMR and U5MR. However, in the cases of MMR and Death Rate Due to RTI, the forecasted values exceeded 2030 targets. This indicates that Bangladesh faces challenges in meeting the 2030 targets for MMR and Death Rate Due to RTI.

conclusionThe analyses underscore the importance of SDG 3 in Bangladesh and its progress towards ensuring healthy lives and well-being for all. While there is optimism regarding NMR and U5MR, more focused efforts may be needed to address the challenges posed by MMR and Death Rate Due to RTI to align with the 2030 targets. This study contributes valuable insights into Bangladesh's journey toward sustainable development in the realm of health and well-being.

Indexed as

Machine LearningSustainable DevelopmentAlgorithmsBangladeshChild MortalityChild, PreschoolFemaleForecastingHumansInfantInfant MortalityInfant, NewbornMaternal MortalityNeural Networks, Computer

Identifiers

PMID40036282
PMCPMC11878945

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