• Ashis Talukder and Benojir Ahammed.
• Statistics Discipline, Khulna University, Khulna, Bangladesh. Electronic address: ashistalukder27@yahoo.com.
• Nutrition. 2020 Oct 1; 78: 110861.

ObjectiveThe aim of this study was is to predict malnutrition status in under-five children in Bangladesh by using various machine learning (ML) algorithms.MethodsFor analysis purposes, the nationally representative secondary records from the 2014 Bangladesh Demographic and Health Survey (BDHS) were used. Five well-known ML algorithms such as linear discriminant analysis (LDA), k-nearest neighbors (k-NN), support vector machines (SVM), random forest (RF), and logistic regression (LR) have been considered to accurately predict malnutrition status among children. Additionally, a systematic assessment of the algorithms was performed by using accuracy, sensitivity, specificity, and Cohen's κ statistic.ResultsBased on various performance parameters, the best results were accomplished with the RF algorithm, which demonstrated an accuracy of 68.51%, a sensitivity of 94.66%, and a specificity of 69.76%. Additionally, a most extreme discriminative ability appeared by RF classification (Cohen's κ = 0.2434).ConclusionOn the basis of the findings, we can presume that the RF algorithm was moderately superior to any other ML algorithms used in this study to predict malnutrition status among under-five children in Bangladesh. Finally, the present research recommends applying RF classification with RF feature selection when the prediction of malnutrition is the core interest.Copyright © 2020 Elsevier Inc. All rights reserved.

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