Prediction of stunting and its socioeconomic determinants among adolescent girls in Ethiopia using machine learning algorithms.
<h4>Background</h4>Stunting is a vital indicator of chronic undernutrition that reveals a failure to reach linear growth. Investigating growth and nutrition status during adolescence, in addition to infancy and childhood is very crucial. However, the available studies in Ethiopia have be...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0316452 |
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| author | Alemu Birara Zemariam Biruk Beletew Abate Addis Wondmagegn Alamaw Eyob Shitie Lake Gizachew Yilak Mulat Ayele Befkad Derese Tilahun Habtamu Setegn Ngusie |
| author_facet | Alemu Birara Zemariam Biruk Beletew Abate Addis Wondmagegn Alamaw Eyob Shitie Lake Gizachew Yilak Mulat Ayele Befkad Derese Tilahun Habtamu Setegn Ngusie |
| author_sort | Alemu Birara Zemariam |
| collection | DOAJ |
| description | <h4>Background</h4>Stunting is a vital indicator of chronic undernutrition that reveals a failure to reach linear growth. Investigating growth and nutrition status during adolescence, in addition to infancy and childhood is very crucial. However, the available studies in Ethiopia have been usually focused in early childhood and they used the traditional stastical methods. Therefore, this study aimed to employ multiple machine learning algorithms to identify the most effective model for the prediction of stunting among adolescent girls in Ethiopia.<h4>Methods</h4>A total of 3156 weighted samples of adolescent girls aged 15-19 years were used from the 2016 Ethiopian Demographic and Health Survey dataset. The data was pre-processed, and 80% and 20% of the observations were used for training, and testing the model, respectively. Eight machine learning algorithms were included for consideration of model building and comparison. The performance of the predictive model was evaluated using evaluation metrics value through Python software. The synthetic minority oversampling technique was used for data balancing and Boruta algorithm was used to identify best features. Association rule mining using an Apriori algorithm was employed to generate the best rule for the association between the independent feature and the targeted feature using R software.<h4>Results</h4>The random forest classifier (sensitivity = 81%, accuracy = 77%, precision = 75%, f1-score = 78%, AUC = 85%) outperformed in predicting stunting compared to other ML algorithms considered in this study. Region, poor wealth index, no formal education, unimproved toilet facility, rural residence, not used contraceptive method, religion, age, no media exposure, occupation, and having one or more children were the top attributes to predict stunting. Association rule mining was identified the top seven best rules that most frequently associated with stunting among adolescent girls in Ethiopia.<h4>Conclusion</h4>The random forest classifier outperformed in predicting and identifying the relevant predictors of stunting. Results have shown that machine learning algorithms can accurately predict stunting, making them potentially valuable as decision-support tools for the relevant stakeholders and giving emphasis for the identified predictors could be an important intervention to halt stunting among adolescent girls. |
| format | Article |
| id | doaj-art-116f8cedea644f96ad47d43889098840 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-116f8cedea644f96ad47d438890988402025-02-05T05:32:11ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031645210.1371/journal.pone.0316452Prediction of stunting and its socioeconomic determinants among adolescent girls in Ethiopia using machine learning algorithms.Alemu Birara ZemariamBiruk Beletew AbateAddis Wondmagegn AlamawEyob Shitie LakeGizachew YilakMulat AyeleBefkad Derese TilahunHabtamu Setegn Ngusie<h4>Background</h4>Stunting is a vital indicator of chronic undernutrition that reveals a failure to reach linear growth. Investigating growth and nutrition status during adolescence, in addition to infancy and childhood is very crucial. However, the available studies in Ethiopia have been usually focused in early childhood and they used the traditional stastical methods. Therefore, this study aimed to employ multiple machine learning algorithms to identify the most effective model for the prediction of stunting among adolescent girls in Ethiopia.<h4>Methods</h4>A total of 3156 weighted samples of adolescent girls aged 15-19 years were used from the 2016 Ethiopian Demographic and Health Survey dataset. The data was pre-processed, and 80% and 20% of the observations were used for training, and testing the model, respectively. Eight machine learning algorithms were included for consideration of model building and comparison. The performance of the predictive model was evaluated using evaluation metrics value through Python software. The synthetic minority oversampling technique was used for data balancing and Boruta algorithm was used to identify best features. Association rule mining using an Apriori algorithm was employed to generate the best rule for the association between the independent feature and the targeted feature using R software.<h4>Results</h4>The random forest classifier (sensitivity = 81%, accuracy = 77%, precision = 75%, f1-score = 78%, AUC = 85%) outperformed in predicting stunting compared to other ML algorithms considered in this study. Region, poor wealth index, no formal education, unimproved toilet facility, rural residence, not used contraceptive method, religion, age, no media exposure, occupation, and having one or more children were the top attributes to predict stunting. Association rule mining was identified the top seven best rules that most frequently associated with stunting among adolescent girls in Ethiopia.<h4>Conclusion</h4>The random forest classifier outperformed in predicting and identifying the relevant predictors of stunting. Results have shown that machine learning algorithms can accurately predict stunting, making them potentially valuable as decision-support tools for the relevant stakeholders and giving emphasis for the identified predictors could be an important intervention to halt stunting among adolescent girls.https://doi.org/10.1371/journal.pone.0316452 |
| spellingShingle | Alemu Birara Zemariam Biruk Beletew Abate Addis Wondmagegn Alamaw Eyob Shitie Lake Gizachew Yilak Mulat Ayele Befkad Derese Tilahun Habtamu Setegn Ngusie Prediction of stunting and its socioeconomic determinants among adolescent girls in Ethiopia using machine learning algorithms. PLoS ONE |
| title | Prediction of stunting and its socioeconomic determinants among adolescent girls in Ethiopia using machine learning algorithms. |
| title_full | Prediction of stunting and its socioeconomic determinants among adolescent girls in Ethiopia using machine learning algorithms. |
| title_fullStr | Prediction of stunting and its socioeconomic determinants among adolescent girls in Ethiopia using machine learning algorithms. |
| title_full_unstemmed | Prediction of stunting and its socioeconomic determinants among adolescent girls in Ethiopia using machine learning algorithms. |
| title_short | Prediction of stunting and its socioeconomic determinants among adolescent girls in Ethiopia using machine learning algorithms. |
| title_sort | prediction of stunting and its socioeconomic determinants among adolescent girls in ethiopia using machine learning algorithms |
| url | https://doi.org/10.1371/journal.pone.0316452 |
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