A Systematic Review of the Accuracy of Digital Applications in Calculating and Assessing Nutritional Status of Children Under Five Years
Background: The increasing utilization of digital applications to analyze the nutritional status of children under five years offers significant progress in public health. However, the accuracy and precision of these tools continue to be a concern due to variations in data quality and user proficiency.
Objectives: This study aims to systematically evaluate the accuracy and precision of digital applications in calculating and assessing the nutritional status of children under five years.
Methods: A comprehensive systematic review was conducted by searching PubMed, ScienceDirect, and Google Scholar databases for relevant studies published between 2010 and 2024. The PRISMA guidelines were followed to identify, screen, and select studies. Key aspects such as algorithm types, data input methods, and user proficiency were analyzed.
Results: The findings show that advanced algorithms, particularly K-Nearest Neighbor (K-NN) and other machine learning models, exhibit high accuracy when supported by quality data and adequate user training. Moreover, real-time IoT-based tools demonstrate high precision in nutritional assessments. Challenges remain in ensuring accurate data entry and algorithm updates to meet the needs of diverse populations.
Conclusions: Digital applications present promising accuracy and precision in evaluating the nutritional status of children under five. However, continuous improvement in data quality and user training is essential for optimal implementation in public health interventions.
Keywords: Child, Accuracy, Precision, Digital Application
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