Ensemble learning to predict short birth interval among reproductive-age women in Ethiopia: evidence from EDHS 2016–2019
Abstract Background A birth interval of less than 33 months was considered short, and in low- income countries like Ethiopia, a short birth interval is the primary cause of approximately 822 maternal deaths every day. Due to that this study aimed to predict short birth interval and associated factor...
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BMC
2025-02-01
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| Series: | BMC Pregnancy and Childbirth |
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| Online Access: | https://doi.org/10.1186/s12884-025-07248-1 |
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| author | Jenberu Mekurianew Kelkay Deje Sendek Anteneh Henok Dessie Wubneh Abraham Dessie Gessesse Gebeyehu Fassil Gebeyehu Kalkidan Kassahun Aweke Mikiyas Birhanu Ejigu Mathias Amare Sendeku Kirubel Adrissie Barkneh Hasset Girma Demissie Wubshet D. Negash Birku Getie Mihret |
| author_facet | Jenberu Mekurianew Kelkay Deje Sendek Anteneh Henok Dessie Wubneh Abraham Dessie Gessesse Gebeyehu Fassil Gebeyehu Kalkidan Kassahun Aweke Mikiyas Birhanu Ejigu Mathias Amare Sendeku Kirubel Adrissie Barkneh Hasset Girma Demissie Wubshet D. Negash Birku Getie Mihret |
| author_sort | Jenberu Mekurianew Kelkay |
| collection | DOAJ |
| description | Abstract Background A birth interval of less than 33 months was considered short, and in low- income countries like Ethiopia, a short birth interval is the primary cause of approximately 822 maternal deaths every day. Due to that this study aimed to predict short birth interval and associated factors among women (15–49) in Ethiopia using ensemble learning algorithms. Methods A secondary data analysis of Ethiopian demographic health servey from 2016 to 2019 was performed. a total of weighted sample of 12,573 women in the reproductive age group was included in this study. Data have been extracted and processed with Stata version 17. The dataset was then imported into a Jupyter notebook for further detailed analysis and visualization. An ensemble Machin learning algorithm using different classification models were implemented. All analysis and calculation were performed using Python 3 programming language in Jupyter Notebook using imblearn, sklearn, and xgboost pakages. Results Random forest demonstrated the best performance with an accuracy 97.84%, recall of 99.70%, F1-score of 97.81%, 98.95% precision on test data and AUC (98%). Region, residency, age of women, sex of child, respondent education, distance health facility, husband education and religion were top predicting factors of short birth interval among women in Ethiopia. Conclusion Random forest was best predictive models with improved performance. "The most significant features that contribute to the accuracy of the top-performing models, notably the Random Forest should be highlighted because they outperformed the other model in the analysis.In general, ensemble learning algorithms can accurately predict short birth interval status, making them potentially useful as decision-support tools for the pertinent stakeholders. |
| format | Article |
| id | doaj-art-cab071c268514a1ba6dd0bd1b8d3f92a |
| institution | Kabale University |
| issn | 1471-2393 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Pregnancy and Childbirth |
| spelling | doaj-art-cab071c268514a1ba6dd0bd1b8d3f92a2025-02-09T12:59:16ZengBMCBMC Pregnancy and Childbirth1471-23932025-02-0125111410.1186/s12884-025-07248-1Ensemble learning to predict short birth interval among reproductive-age women in Ethiopia: evidence from EDHS 2016–2019Jenberu Mekurianew Kelkay0Deje Sendek Anteneh1Henok Dessie Wubneh2Abraham Dessie Gessesse3Gebeyehu Fassil Gebeyehu4Kalkidan Kassahun Aweke5Mikiyas Birhanu Ejigu6Mathias Amare Sendeku7Kirubel Adrissie Barkneh8Hasset Girma Demissie9Wubshet D. Negash10Birku Getie Mihret11Department of Health Informatics, College of Health Sciences, Debark UniversityDepartment of Health Informatics, Institute of Public Health, College of Medicine and Health Science, University of GondarDepartment of Health Informatics, Institute of Public Health, College of Medicine and Health Science, University of GondarDepartment of Pediatric and Child Health Nursing, College of Health Sciences, Woldia UniversitySchool of Medicine, College of Medicine and Health Sciences, Bahir Dar UniversitySchool of Medicine, College of Medicine and Health Sciences, Bahir Dar UniversitySchool of Medicine, College of Medicine and Health Sciences, Bahir Dar UniversitySchool of Medicine, College of Medicine and Health Sciences, Bahir Dar UniversitySchool of Medicine, College of Medicine and Health Sciences, Bahir Dar UniversitySchool of Medicine, College of Medicine and Health Sciences, Bahir Dar UniversityDepartment of Health Systems and Policy, Institute of Public Health, College of Medicine and Health Sciences, University of GondarDepartment of Computer Sciences, College of Natural and Computational Sciences, Debark UniversityAbstract Background A birth interval of less than 33 months was considered short, and in low- income countries like Ethiopia, a short birth interval is the primary cause of approximately 822 maternal deaths every day. Due to that this study aimed to predict short birth interval and associated factors among women (15–49) in Ethiopia using ensemble learning algorithms. Methods A secondary data analysis of Ethiopian demographic health servey from 2016 to 2019 was performed. a total of weighted sample of 12,573 women in the reproductive age group was included in this study. Data have been extracted and processed with Stata version 17. The dataset was then imported into a Jupyter notebook for further detailed analysis and visualization. An ensemble Machin learning algorithm using different classification models were implemented. All analysis and calculation were performed using Python 3 programming language in Jupyter Notebook using imblearn, sklearn, and xgboost pakages. Results Random forest demonstrated the best performance with an accuracy 97.84%, recall of 99.70%, F1-score of 97.81%, 98.95% precision on test data and AUC (98%). Region, residency, age of women, sex of child, respondent education, distance health facility, husband education and religion were top predicting factors of short birth interval among women in Ethiopia. Conclusion Random forest was best predictive models with improved performance. "The most significant features that contribute to the accuracy of the top-performing models, notably the Random Forest should be highlighted because they outperformed the other model in the analysis.In general, ensemble learning algorithms can accurately predict short birth interval status, making them potentially useful as decision-support tools for the pertinent stakeholders.https://doi.org/10.1186/s12884-025-07248-1Short birth intervalEnsemble learningWomenEthiopia |
| spellingShingle | Jenberu Mekurianew Kelkay Deje Sendek Anteneh Henok Dessie Wubneh Abraham Dessie Gessesse Gebeyehu Fassil Gebeyehu Kalkidan Kassahun Aweke Mikiyas Birhanu Ejigu Mathias Amare Sendeku Kirubel Adrissie Barkneh Hasset Girma Demissie Wubshet D. Negash Birku Getie Mihret Ensemble learning to predict short birth interval among reproductive-age women in Ethiopia: evidence from EDHS 2016–2019 BMC Pregnancy and Childbirth Short birth interval Ensemble learning Women Ethiopia |
| title | Ensemble learning to predict short birth interval among reproductive-age women in Ethiopia: evidence from EDHS 2016–2019 |
| title_full | Ensemble learning to predict short birth interval among reproductive-age women in Ethiopia: evidence from EDHS 2016–2019 |
| title_fullStr | Ensemble learning to predict short birth interval among reproductive-age women in Ethiopia: evidence from EDHS 2016–2019 |
| title_full_unstemmed | Ensemble learning to predict short birth interval among reproductive-age women in Ethiopia: evidence from EDHS 2016–2019 |
| title_short | Ensemble learning to predict short birth interval among reproductive-age women in Ethiopia: evidence from EDHS 2016–2019 |
| title_sort | ensemble learning to predict short birth interval among reproductive age women in ethiopia evidence from edhs 2016 2019 |
| topic | Short birth interval Ensemble learning Women Ethiopia |
| url | https://doi.org/10.1186/s12884-025-07248-1 |
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