Prediction of zero-dose children using supervised machine learning algorithm in Tanzania: evidence f...
Prediction of zero-dose children using supervised machine learning algorithm in Tanzania: evidence from the recent 2022 Tanzania Demographic and Health Survey
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England: British Medical Journal Publishing Group
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English
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England: British Medical Journal Publishing Group
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ObjectivesThis study aimed to employ machine learning algorithms to predict the factors contributing to zero-dose children in Tanzania, using the most recent nationally representative data.DesignCross-sectional study.SettingThis study was conducted in Tanzania and used the most recent 2022 Tanzania Demographic and Health Survey, accessed from http://www.dhsprogram.com.ParticipantsA total of 2120 children aged 12–23 months were included in this study.Outcome measureSeven classification algorithms were used in this study: logistic regression, decision tree classifier, random forest classifier (RF), support vector machine, K-nearest neighbour, XGBoost (XGB) and Naive Bayes. The dataset was randomly divided into training and...
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Prediction of zero-dose children using supervised machine learning algorithm in Tanzania: evidence from the recent 2022 Tanzania Demographic and Health Survey
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TN_cdi_doaj_primary_oai_doaj_org_article_af2bf63b94ae444f8dde7f23299b4817
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_af2bf63b94ae444f8dde7f23299b4817
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ISSN
2044-6055
E-ISSN
2044-6055
DOI
10.1136/bmjopen-2024-097395
