The application of machine learning approaches to classify and predict fertility rate in Ethiopia
Abstract Integrating machine learning (ML) models into healthcare systems is a rapidly evolving field with the potential to revolutionize care delivery. This study aimed to classify fertility rates and identify significant predictors using ML models among reproductive women in Ethiopia. This study u...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-85695-8 |
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| author | Ewunate Assaye Kassaw Biruk Beletew Abate Bekele Mulat Enyew Ashenafi Kibret Sendekie |
| author_facet | Ewunate Assaye Kassaw Biruk Beletew Abate Bekele Mulat Enyew Ashenafi Kibret Sendekie |
| author_sort | Ewunate Assaye Kassaw |
| collection | DOAJ |
| description | Abstract Integrating machine learning (ML) models into healthcare systems is a rapidly evolving field with the potential to revolutionize care delivery. This study aimed to classify fertility rates and identify significant predictors using ML models among reproductive women in Ethiopia. This study utilized eight ML models in 5864 reproductive-age women using Ethiopian Demographic Health Survey (EDHS), 2019 data. Phyton programming language was used to develop these models. Predictors of fertility rate were determined using the feature important techniques. The performance of models was evaluated using accuracy, area under the curve (AUC), precision, recall, F1-score, specificity, and sensitivity. The mean age of participants was 32.7 (± 5.6) years. The random forest classifier (accuracy = 0.901 and AUC = 0.961) followed by a one-dimensional convolutional neural network (accuracy = 0.899 and AUC = 0.958), logistic regression (accuracy = 0.874 and AUC = 0.937), and gradient boost classifier (accuracy = 0.851 and AUC 0.927) were the top performing ML models. Family size, age, occupation, and education with an average importance score of 0.198, 0.151, 0.118, and 0.081, respectively were the top significant predictors of the fertility rate. The best ML models to classify and predict fertility rates were random forest, one-dimensional convolutional neural network, logistic regression, and gradient boost classifier. The findings on important factors of fertility rate can inform targeted public health, programs that address disparities related to family size, occupation, education, and other socioeconomic factors. |
| format | Article |
| id | doaj-art-d98d905e346141b1b6ae92452a331ec4 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-d98d905e346141b1b6ae92452a331ec42025-01-26T12:31:19ZengNature PortfolioScientific Reports2045-23222025-01-0115111310.1038/s41598-025-85695-8The application of machine learning approaches to classify and predict fertility rate in Ethiopia Ewunate Assaye Kassaw0Biruk Beletew Abate1Bekele Mulat Enyew2Ashenafi Kibret Sendekie3Department of Biomedical Engineering, Institute of Technology, University of GondarCollege of Medicine and Health Sciences, Woldia UniversityDepartment of Information Technology, College of Informatics, University of GondarDepartment of Clinical Pharmacy, School of Pharmacy, College of Medicine and Health Sciences, University of GondarAbstract Integrating machine learning (ML) models into healthcare systems is a rapidly evolving field with the potential to revolutionize care delivery. This study aimed to classify fertility rates and identify significant predictors using ML models among reproductive women in Ethiopia. This study utilized eight ML models in 5864 reproductive-age women using Ethiopian Demographic Health Survey (EDHS), 2019 data. Phyton programming language was used to develop these models. Predictors of fertility rate were determined using the feature important techniques. The performance of models was evaluated using accuracy, area under the curve (AUC), precision, recall, F1-score, specificity, and sensitivity. The mean age of participants was 32.7 (± 5.6) years. The random forest classifier (accuracy = 0.901 and AUC = 0.961) followed by a one-dimensional convolutional neural network (accuracy = 0.899 and AUC = 0.958), logistic regression (accuracy = 0.874 and AUC = 0.937), and gradient boost classifier (accuracy = 0.851 and AUC 0.927) were the top performing ML models. Family size, age, occupation, and education with an average importance score of 0.198, 0.151, 0.118, and 0.081, respectively were the top significant predictors of the fertility rate. The best ML models to classify and predict fertility rates were random forest, one-dimensional convolutional neural network, logistic regression, and gradient boost classifier. The findings on important factors of fertility rate can inform targeted public health, programs that address disparities related to family size, occupation, education, and other socioeconomic factors.https://doi.org/10.1038/s41598-025-85695-8ClassificationFertility rateMachine learningPredictionEthiopiaEDHS data |
| spellingShingle | Ewunate Assaye Kassaw Biruk Beletew Abate Bekele Mulat Enyew Ashenafi Kibret Sendekie The application of machine learning approaches to classify and predict fertility rate in Ethiopia Scientific Reports Classification Fertility rate Machine learning Prediction Ethiopia EDHS data |
| title | The application of machine learning approaches to classify and predict fertility rate in Ethiopia |
| title_full | The application of machine learning approaches to classify and predict fertility rate in Ethiopia |
| title_fullStr | The application of machine learning approaches to classify and predict fertility rate in Ethiopia |
| title_full_unstemmed | The application of machine learning approaches to classify and predict fertility rate in Ethiopia |
| title_short | The application of machine learning approaches to classify and predict fertility rate in Ethiopia |
| title_sort | application of machine learning approaches to classify and predict fertility rate in ethiopia |
| topic | Classification Fertility rate Machine learning Prediction Ethiopia EDHS data |
| url | https://doi.org/10.1038/s41598-025-85695-8 |
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