A machine learning classifier approach for identifying the determinants of under-five child undernutrition in Ethiopian administrative zones.
Background: Undernutrition is the main cause of child death in developing countries. This paper aimed to explore the efficacy of machine learning (ML) approaches in predicting under-five undernutrition in Ethiopian administrative zones and to identify the most important predictors.Method: The study...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 21; no. 1; pp. 1 - 13 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
10/25/2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=153205659&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153205659 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 10/25/2021 vid: 21 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 153205659 153205659 NLM34689769 153205659 10.1186/s12911-021-01652-1 NLM34689769 153205659 ppf: 1 ppct: 12 formats: tig: atl: A machine learning classifier approach for identifying the determinants of under-five child undernutrition in Ethiopian administrative zones. aug: au: Fenta, Haile Mekonnen Zewotir, Temesgen Muluneh, Essey Kebede affil: Department of Statistics, College of Science, Bahir Dar University, Bahir Dar, Ethiopia sug: subj: Child Nutrition Disorders Diagnosis Undernutrition Child Nutrition Disorders Epidemiology Cross Sectional Studies Child Retrospective Design Questionnaires Child: 6-12 years ab: Background: Undernutrition is the main cause of child death in developing countries. This paper aimed to explore the efficacy of machine learning (ML) approaches in predicting under-five undernutrition in Ethiopian administrative zones and to identify the most important predictors.Method: The study employed ML techniques using retrospective cross-sectional survey data from Ethiopia, a national-representative data collected in the year (2000, 2005, 2011, and 2016). We explored six commonly used ML algorithms; Logistic regression, Least Absolute Shrinkage and Selection Operator (L-1 regularization logistic regression), L-2 regularization (Ridge), Elastic net, neural network, and random forest (RF). Sensitivity, specificity, accuracy, and area under the curve were used to evaluate the performance of those models.Results: Based on different performance evaluations, the RF algorithm was selected as the best ML model. In the order of importance; urban-rural settlement, literacy rate of parents, and place of residence were the major determinants of disparities of nutritional status for under-five children among Ethiopian administrative zones.Conclusion: Our results showed that the considered machine learning classification algorithms can effectively predict the under-five undernutrition status in Ethiopian administrative zones. Persistent under-five undernutrition status was found in the northern part of Ethiopia. The identification of such high-risk zones could provide useful information to decision-makers trying to reduce child undernutrition. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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