Performance of Machine Learning Classifiers in Classifying Stunting among Under-Five Children in Zambia

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)...

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Bibliographic Details
Main Authors: Obvious Nchimunya Chilyabanyama, Roma Chilengi, Michelo Simuyandi, Caroline C. Chisenga, Masuzyo Chirwa, Kalongo Hamusonde, Rakesh Kumar Saroj, Najeeha Talat Iqbal, Innocent Ngaruye, Samuel Bosomprah
Format: Article
Language:English
Published: MDPI AG 2022-07-01
Series:Children
Subjects:
stunting
machine learning
random forest
Naïve Bayesian
ZDHS
Online Access:https://www.mdpi.com/2227-9067/9/7/1082

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