Predicting the level of anemia among Ethiopian pregnant women using homogeneous ensemble machine learning algorithm
Abstract Background More than 115,000 maternal deaths and 591,000 prenatal deaths occurred in the world per year with anemia, the reduction of red blood cells or hemoglobin in the blood. The world health organization divides anemia in pregnancy into mild anemia (Hb 10–10.9 g/dl), moderate anemia (Hb...
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| Format: | Article |
| Language: | English |
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BMC
2022-09-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-022-01992-6 |
| _version_ | 1811208786671566848 |
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| author | Belayneh Endalamaw Dejene Tesfamariam M. Abuhay Dawit Shibabaw Bogale |
| author_facet | Belayneh Endalamaw Dejene Tesfamariam M. Abuhay Dawit Shibabaw Bogale |
| author_sort | Belayneh Endalamaw Dejene |
| collection | DOAJ |
| description | Abstract Background More than 115,000 maternal deaths and 591,000 prenatal deaths occurred in the world per year with anemia, the reduction of red blood cells or hemoglobin in the blood. The world health organization divides anemia in pregnancy into mild anemia (Hb 10–10.9 g/dl), moderate anemia (Hb 7.0–9.9 g/dl), and severe anemia (Hb < 7 g/dl). This study aims to predict the level of anemia among pregnant women in the case of Ethiopia using homogeneous ensemble machine learning algorithms. Methods This study was conducted following a design science approach. The data were gathered from the Ethiopian demographic health survey and preprocessed to get quality data that are suitable for the machine learning algorithm to develop a model that predicts the levels of anemia among pregnant. Decision tree, random forest, cat boost, and extreme gradient boosting with class decomposition (one versus one and one versus rest) and without class decomposition were employed to build the predictive model. For constructing the proposed model, twelve experiments were conducted with a total of 29,104 instances with 23 features, and a training and testing dataset split ratio of 80/20. Results The overall accuracy of random forest, extreme gradient boosting, and cat boost without class decompositions is 91.34%, 94.26%, and 97.08.90%, respectively. The overall accuracy of random forest, extreme gradient boosting, and cat boost with one versus one is 94.4%, 95.21%, and 97.44%, respectively. The overall accuracy of random forest, extreme gradient boosting, and cat boost with one versus the rest are 94.4%, 94.54%, and 97.6%, respectively. Conclusion Finally, the researcher decided to use cat boost algorithms with one versus the rest for further use in the development of artifacts, model deployment, risk factor analysis, and generating rules because it has registered better performance with 97.6% accuracy. The most determinant risk factors of anemia among pregnant women were identified using feature importance. Some of them are the duration of the current pregnancy, age, source of drinking water, respondent’s (pregnant women) occupation, number of household members, wealth index, husband/partner's education level, and birth history. |
| first_indexed | 2024-04-12T04:27:29Z |
| format | Article |
| id | doaj.art-29c6e0fbb6de4332a982f922b413e687 |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-04-12T04:27:29Z |
| publishDate | 2022-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-29c6e0fbb6de4332a982f922b413e6872022-12-22T03:48:01ZengBMCBMC Medical Informatics and Decision Making1472-69472022-09-0122111110.1186/s12911-022-01992-6Predicting the level of anemia among Ethiopian pregnant women using homogeneous ensemble machine learning algorithmBelayneh Endalamaw Dejene0Tesfamariam M. Abuhay1Dawit Shibabaw Bogale2College of Informatics, University of GondarCollege of Informatics, University of GondarCollege of Informatics, University of GondarAbstract Background More than 115,000 maternal deaths and 591,000 prenatal deaths occurred in the world per year with anemia, the reduction of red blood cells or hemoglobin in the blood. The world health organization divides anemia in pregnancy into mild anemia (Hb 10–10.9 g/dl), moderate anemia (Hb 7.0–9.9 g/dl), and severe anemia (Hb < 7 g/dl). This study aims to predict the level of anemia among pregnant women in the case of Ethiopia using homogeneous ensemble machine learning algorithms. Methods This study was conducted following a design science approach. The data were gathered from the Ethiopian demographic health survey and preprocessed to get quality data that are suitable for the machine learning algorithm to develop a model that predicts the levels of anemia among pregnant. Decision tree, random forest, cat boost, and extreme gradient boosting with class decomposition (one versus one and one versus rest) and without class decomposition were employed to build the predictive model. For constructing the proposed model, twelve experiments were conducted with a total of 29,104 instances with 23 features, and a training and testing dataset split ratio of 80/20. Results The overall accuracy of random forest, extreme gradient boosting, and cat boost without class decompositions is 91.34%, 94.26%, and 97.08.90%, respectively. The overall accuracy of random forest, extreme gradient boosting, and cat boost with one versus one is 94.4%, 95.21%, and 97.44%, respectively. The overall accuracy of random forest, extreme gradient boosting, and cat boost with one versus the rest are 94.4%, 94.54%, and 97.6%, respectively. Conclusion Finally, the researcher decided to use cat boost algorithms with one versus the rest for further use in the development of artifacts, model deployment, risk factor analysis, and generating rules because it has registered better performance with 97.6% accuracy. The most determinant risk factors of anemia among pregnant women were identified using feature importance. Some of them are the duration of the current pregnancy, age, source of drinking water, respondent’s (pregnant women) occupation, number of household members, wealth index, husband/partner's education level, and birth history.https://doi.org/10.1186/s12911-022-01992-6Homogeneous ensemble machine learningHealth informaticsAnemiaMaternal healthcare |
| spellingShingle | Belayneh Endalamaw Dejene Tesfamariam M. Abuhay Dawit Shibabaw Bogale Predicting the level of anemia among Ethiopian pregnant women using homogeneous ensemble machine learning algorithm BMC Medical Informatics and Decision Making Homogeneous ensemble machine learning Health informatics Anemia Maternal healthcare |
| title | Predicting the level of anemia among Ethiopian pregnant women using homogeneous ensemble machine learning algorithm |
| title_full | Predicting the level of anemia among Ethiopian pregnant women using homogeneous ensemble machine learning algorithm |
| title_fullStr | Predicting the level of anemia among Ethiopian pregnant women using homogeneous ensemble machine learning algorithm |
| title_full_unstemmed | Predicting the level of anemia among Ethiopian pregnant women using homogeneous ensemble machine learning algorithm |
| title_short | Predicting the level of anemia among Ethiopian pregnant women using homogeneous ensemble machine learning algorithm |
| title_sort | predicting the level of anemia among ethiopian pregnant women using homogeneous ensemble machine learning algorithm |
| topic | Homogeneous ensemble machine learning Health informatics Anemia Maternal healthcare |
| url | https://doi.org/10.1186/s12911-022-01992-6 |
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