Socioeconomic and demographic factors associated with anaemia among women of reproductive age in Zimbabwe: a supervised machine learning approach
Abstract Anaemia affects approximately one-third of women of reproductive age globally, with the highest burden observed in resource-limited countries. Therefore, this study aimed to determine the socioeconomic and demographic factors associated with anaemia and predict anaemia among women in Zimbab...
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Springer
2025-04-01
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| Series: | Discover Public Health |
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| Online Access: | https://doi.org/10.1186/s12982-025-00524-7 |
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| author | Garikayi Chemhaka Elliot Mbunge Tafadzwa Dzinamarira Godfrey Musuka John Batani Benhildah Muchemwa Stephen Fashoto Munyaradzi Mapingure Rutendo Birri Makota Ester Petrus |
| author_facet | Garikayi Chemhaka Elliot Mbunge Tafadzwa Dzinamarira Godfrey Musuka John Batani Benhildah Muchemwa Stephen Fashoto Munyaradzi Mapingure Rutendo Birri Makota Ester Petrus |
| author_sort | Garikayi Chemhaka |
| collection | DOAJ |
| description | Abstract Anaemia affects approximately one-third of women of reproductive age globally, with the highest burden observed in resource-limited countries. Therefore, this study aimed to determine the socioeconomic and demographic factors associated with anaemia and predict anaemia among women in Zimbabwe. Using nationally representative, cross-sectional data from the 2015 Zimbabwe Demographic and Health Survey (DHS), a dataset from a sample of 5412 women of reproductive age was analyzed. The Chi-square test and multivariate logistic regression were employed to identify independent predictors of anaemia, while Elastic Net was used for feature importance scoring. To address the class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied. The prevalence of anaemia among women in Zimbabwe was 24.1%. Multivariate logistic regression revealed significant associations between anaemia and several factors, including older age (35–49 years) (adjusted Odds Ratio [aOR] = 1.31), marital status (being married) (aOR = 0.72), higher education (aOR = 0.47), middle household wealth (aOR = 1.32), professional occupation (aOR = 1.60), current use of modern contraceptives (aOR = 0.59), and overweight/obesity (aOR = 0.56). The highest burden was observed in Matabeleland South province (aOR = 3.44). Among prediction models, the random forest classifier outperformed K-Nearest Neighbors (KNN) and decision trees, achieving an accuracy of 74%, recall of 78%, F1-score of 75%, precision of 72%, and an Area Under the Curve (AUC) of 81.5%. Targeted interventions focusing on key socioeconomic and demographic characteristics could help reduce anaemia in women of reproductive age. Predictive models can aid healthcare practitioners in identifying at-risk individuals and implementing timely interventions to mitigate the impact of anaemia. |
| format | Article |
| id | doaj-art-85b6f8b9a4fc406f845cdbe412ae7604 |
| institution | OA Journals |
| issn | 3005-0774 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Public Health |
| spelling | doaj-art-85b6f8b9a4fc406f845cdbe412ae76042025-08-20T02:16:56ZengSpringerDiscover Public Health3005-07742025-04-0122111710.1186/s12982-025-00524-7Socioeconomic and demographic factors associated with anaemia among women of reproductive age in Zimbabwe: a supervised machine learning approachGarikayi Chemhaka0Elliot Mbunge1Tafadzwa Dzinamarira2Godfrey Musuka3John Batani4Benhildah Muchemwa5Stephen Fashoto6Munyaradzi Mapingure7Rutendo Birri Makota8Ester Petrus9Department of Statistics and Demography, Faculty of Social Sciences, University of EswatiniDivision of Research, Innovation and Engagement, Mangosuthu University of TechnologySchool of Health Systems and Public Health, University of PretoriaInternational Initiative for Impact EvaluationFaculty of Engineering and Technology, Botho UniversityDepartment of Computer Science, Faculty of Science and Engineering, University of EswatiniDepartment of Computer Science, Faculty of Science and Engineering, University of EswatiniICAP in ZimbabweDepartment of Biological Sciences and Ecology, University of ZimbabweSoftware Department, Faculty of ICT, International University of ManagementAbstract Anaemia affects approximately one-third of women of reproductive age globally, with the highest burden observed in resource-limited countries. Therefore, this study aimed to determine the socioeconomic and demographic factors associated with anaemia and predict anaemia among women in Zimbabwe. Using nationally representative, cross-sectional data from the 2015 Zimbabwe Demographic and Health Survey (DHS), a dataset from a sample of 5412 women of reproductive age was analyzed. The Chi-square test and multivariate logistic regression were employed to identify independent predictors of anaemia, while Elastic Net was used for feature importance scoring. To address the class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied. The prevalence of anaemia among women in Zimbabwe was 24.1%. Multivariate logistic regression revealed significant associations between anaemia and several factors, including older age (35–49 years) (adjusted Odds Ratio [aOR] = 1.31), marital status (being married) (aOR = 0.72), higher education (aOR = 0.47), middle household wealth (aOR = 1.32), professional occupation (aOR = 1.60), current use of modern contraceptives (aOR = 0.59), and overweight/obesity (aOR = 0.56). The highest burden was observed in Matabeleland South province (aOR = 3.44). Among prediction models, the random forest classifier outperformed K-Nearest Neighbors (KNN) and decision trees, achieving an accuracy of 74%, recall of 78%, F1-score of 75%, precision of 72%, and an Area Under the Curve (AUC) of 81.5%. Targeted interventions focusing on key socioeconomic and demographic characteristics could help reduce anaemia in women of reproductive age. Predictive models can aid healthcare practitioners in identifying at-risk individuals and implementing timely interventions to mitigate the impact of anaemia.https://doi.org/10.1186/s12982-025-00524-7AnaemiaMachine learningSurveySocioeconomicDemographicLogistic regression |
| spellingShingle | Garikayi Chemhaka Elliot Mbunge Tafadzwa Dzinamarira Godfrey Musuka John Batani Benhildah Muchemwa Stephen Fashoto Munyaradzi Mapingure Rutendo Birri Makota Ester Petrus Socioeconomic and demographic factors associated with anaemia among women of reproductive age in Zimbabwe: a supervised machine learning approach Discover Public Health Anaemia Machine learning Survey Socioeconomic Demographic Logistic regression |
| title | Socioeconomic and demographic factors associated with anaemia among women of reproductive age in Zimbabwe: a supervised machine learning approach |
| title_full | Socioeconomic and demographic factors associated with anaemia among women of reproductive age in Zimbabwe: a supervised machine learning approach |
| title_fullStr | Socioeconomic and demographic factors associated with anaemia among women of reproductive age in Zimbabwe: a supervised machine learning approach |
| title_full_unstemmed | Socioeconomic and demographic factors associated with anaemia among women of reproductive age in Zimbabwe: a supervised machine learning approach |
| title_short | Socioeconomic and demographic factors associated with anaemia among women of reproductive age in Zimbabwe: a supervised machine learning approach |
| title_sort | socioeconomic and demographic factors associated with anaemia among women of reproductive age in zimbabwe a supervised machine learning approach |
| topic | Anaemia Machine learning Survey Socioeconomic Demographic Logistic regression |
| url | https://doi.org/10.1186/s12982-025-00524-7 |
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