Efficient Machine Learning for Malnutrition Prediction among under-five children in India

Child malnutrition is considered to be one of the leading causes of infant mortality and malnutrition. This study was aimed to leverage the advantages offered by machine learning models in terms of determining and accurately predicting significant factors of malnutrition. For this study, the Children's recode files from the Indian Demographic and Health Survey (IDHS) datasets from 2005–2006 and 2015–2016 were used. To examine the nutritional status of children aged 0–59 months, this study looks at stunting (Height-for-age), wasting (Weight-for-Height), and concurrent stunted wasting (Height-for-age-Weight-for-Height). Regular Machine Learning models, Tabular Deep Learning frameworks, H2O base models, and AutoML models are the four types of machine learning models employed in our research. This research found that Automated machine learning algorithms and Tabular Deep Learning frameworks, in general, outperformed other models in terms of speed and efficiency, as well as Accuracy (up to 96.46%) and AUC-ROC scores (up to 99.95%), which are important in classification problems like this one. Following a graphical representation of the importance of numerous drivers of malnutrition for all three anthropometric indices, we concluded our findings by comparing the performances of several models and determining the top-performing algorithms. This paper significantly contributes to the possibilities of using machine learning in identifying probable correlates of malnutrition for the effective prevention, cure, and identification of target groups.