| Summary: | The monitoring of children's nutritional status serves as a crucial tool for assessing the health of both children and society as a whole. In this regard, machine learning (ML) has been employed to predict nutritional status for monitoring purposes. This topic has been extensively discussed. However, the question remains as to which algorithm or ML framework can yield the highest accuracy in predicting the nutritional status of children within a specific region. Furthermore, determining the appropriate dataset for predictions is also crucial. Therefore, this review aims to identify and analyze the research trends, dataset characteristics, algorithms, and frameworks utilized in studies pertaining to the nutritional status of children under the age of five from 2017 to early 2022. The selected papers focus on the application of ML techniques in predicting nutritional status. The findings of this research reveal that the Bangladesh demographic and health survey 2014 dataset is among the popular choices for ML applications in this field. The most commonly employed algorithms include neural networks, random forests, logistic regression, and decision trees which demonstrated promising performance. Lastly, the data preprocessing stage within a framework plays a significant role in models aimed at predicting nutritional status. © 2024, Institute of Advanced Engineering and Science. All rights reserved.
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