Performance of Machine Learning Classifiers in Classifying Stunting among Under-Five Children in Zambia
Obvious Nchimunya Chilyabanyama,
Roma Chilengi,
Michelo Simuyandi,
Caroline C. Chisenga,
Masuzyo Chirwa,
Kalongo Hamusonde,
Rakesh Kumar Saroj,
Najeeha Talat Iqbal,
Innocent Ngaruye,
Samuel Bosomprah
Affiliations
Obvious Nchimunya Chilyabanyama
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
Roma Chilengi
Enteric Disease and Vaccines Research Unit, Centre for Infectious Disease Research in Zambia, Lusaka P.O. Box 34681, Zambia
Michelo Simuyandi
Enteric Disease and Vaccines Research Unit, Centre for Infectious Disease Research in Zambia, Lusaka P.O. Box 34681, Zambia
Caroline C. Chisenga
Enteric Disease and Vaccines Research Unit, Centre for Infectious Disease Research in Zambia, Lusaka P.O. Box 34681, Zambia
Masuzyo Chirwa
Enteric Disease and Vaccines Research Unit, Centre for Infectious Disease Research in Zambia, Lusaka P.O. Box 34681, Zambia
Kalongo Hamusonde
Enteric Disease and Vaccines Research Unit, Centre for Infectious Disease Research in Zambia, Lusaka P.O. Box 34681, Zambia
Rakesh Kumar Saroj
Department of Community Medicine, Sikkim Manipal Institute of Medical Sciences (SIMMS) Sikkim Manipal University, Gangtok 03592, India
Najeeha Talat Iqbal
Department of Paediatrics and Child Health, Biological and Biomedical Sciences, Aga Khan University Hospital, Karachi 74800, Pakistan
Innocent Ngaruye
College of Science of Technology, University of Rwanda, KN 7 Ave, Kigali P.O. Box 4285, Rwanda
Samuel Bosomprah
Enteric Disease and Vaccines Research Unit, Centre for Infectious Disease Research in Zambia, Lusaka P.O. Box 34681, 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), 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.
