ArticleBMC medical informatics and decision making2022
Predicting the level of anemia among Ethiopian pregnant women using homogeneous ensemble machine learning algorithm.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 45 citations in OpenAlex.
- Multi-class machine learning classification and important features on anemia among women in Tanzania and Rwanda.BMC public health · 2025Article
- AI-assisted haematology: machine learning-based prediction of iron-deficiency anaemia from reticulocyte maturation indices.BMC medical informatics and decision making · 2025Article
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Data science and artificial intelligence for maternal, newborn and child health: scoping review and thematic analysis.BMC public health · 2025Article
- Predicting malnutrition-based anemia in geriatric patients using machine learning methods.Journal of evaluation in clinical practice · 2025Article
- Differential diagnosis of iron deficiency anemia from aplastic anemia using machine learning and explainable Artificial Intelligence utilizing blood attributes.Scientific reports · 2025Article
- The Role of Digital Technology in Preventing Anemia Among Pregnant Women: A Scoping Review.International journal of telemedicine and applications · 2025Review
- Leveraging machine learning models for anemia severity detection among pregnant women following ANC: Ethiopian context.BMC public health · 2024Article
- Real-time non-invasive hemoglobin prediction using deep learning-enabled smartphone imaging.BMC medical informatics and decision making · 2024Article
- Prognostics of multiple malaria episodes and nutritional status in children aged 6 to 59 months from 2013 to 2017 in Dangassa, Koulikoro region, Mali.Malaria journal · 2024Article
- Prognostics of multiple malaria episodes and nutritional status in children aged 6 to 59 months from 2013 to 2017 in Dangassa, Mali.Research square · 2023Article
- A New Artificial Intelligence Approach Using Extreme Learning Machine as the Potentially Effective Model to Predict and Analyze the Diagnosis of Anemia.Healthcare (Basel, Switzerland) · 2023Article
- Boosting the Performance of Artificial Intelligence-Driven Models in Predicting COVID-19 Mortality in Ethiopia.Diagnostics (Basel, Switzerland) · 2023Article
- Detection of iron deficiency anemia by medical images: a comparative study of machine learning algorithms.BioData mining · 2023Article
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Authors and funding
3 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundMore 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.
methodsThis 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.
resultsThe 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.
conclusionFinally, 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.
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