Machine learning algorithms for predicting malnutrition among under-five children in Bangladesh

OBJECTIVE The aim of this study was is to predict malnutrition status in under-five children in Bangladesh by using various machine learning (ML) algorithms. METHODS For 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. RESULTS Based on various performance parameters, the best results were accomplished with the RF algorithm, which demonstrated an accuracy of 68.51%, a sensitivity of…