Performance of Machine Learning Classifiers in Classifying Stunting among Under-Five Children in Zambia.
Stunting is a global public health issue. We sought to train and evaluate machine learning (ML) classification algorithms on the Zambia Demographic Health Survey (ZDHS) dataset to predict stunting among children under the age of five in Zambia. We applied Logistic regression (LR), Random Forest (RF)...
| Publicado en: | Children Vol. 9; no. 7 |
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| Autores principales: | , , , , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
MDPI
Jul2022
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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=158213017&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158213017 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 22279067 LW6K jtl: Children issn: 22279067 maglogo: N pubinfo: dt: Jul2022 vid: 9 iid: 7 pid: 97109 pub: MDPI artinfo: ui: 158213017 158213017 158213017 10.3390/children9071082 158213017 ppct: 11 formats: tig: atl: Performance of Machine Learning Classifiers in Classifying Stunting among Under-Five Children in Zambia. aug: au: Chilyabanyama, Obvious Nchimunya Chilengi, Roma Simuyandi, Michelo Chisenga, Caroline C. Chirwa, Masuzyo Hamusonde, Kalongo Saroj, Rakesh Kumar Iqbal, Najeeha Talat Ngaruye, Innocent Bosomprah, Samuel affil: African Centre of Excellence in Data Science, College of Business Studies Kigali, University of Rwanda, Gikondo—Street, KK 737, Kigali P.O. Box 4285, Rwanda sug: subj: Growth Disorders In Infancy and Childhood Growth Disorders Classification Growth Disorders Risk Factors Risk Assessment Machine Learning Evaluation Algorithms Evaluation Zambia Human Male Female Infant Child, Preschool Logistic Regression Random Forest Probability Nutrition Predictive Value of Tests Infant: 1-23 months Child, Preschool: 2-5 years Male Female ab: Stunting is a global public health issue. We sought to train and evaluate machine learning (ML) classification algorithms on the Zambia Demographic Health Survey (ZDHS) dataset to predict stunting among children under the age of five in Zambia. We applied Logistic regression (LR), Random Forest (RF), SV classification (SVC), XG Boost (XgB) and Naïve Bayes (NB) algorithms to predict the probability of stunting among children under five years of age, on the 2018 ZDHS dataset. We calibrated predicted probabilities and plotted the calibration curves to compare model performance. We computed accuracy, recall, precision and F1 for each machine learning algorithm. About 2327 (34.2%) children were stunted. Thirteen of fifty-eight features were selected for inclusion in the model using random forest. Calibrating the predicted probabilities improved the performance of machine learning algorithms when evaluated using calibration curves. RF was the most accurate algorithm, with an accuracy score of 79% in the testing and 61.6% in the training data while Naïve Bayesian was the worst performing algorithm for predicting stunting among children under five in Zambia using the 2018 ZDHS dataset. ML models aids quick diagnosis of stunting and the timely development of interventions aimed at preventing stunting. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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