Analysis of the Application of Machine Learning Algorithms for Classification of Toddler Nutritional Status Based on Anthropometric Data - Yamasari, Yuni; Yogiyanti, Esti; Yohannes, Ervin (Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics, 2025)
The rapid advancement of technology has required appropriate strategies to achieve accurate and optimal results. Among these, machine learning has become one of the most widely applied technologies across various domains, including healthcare, due to its ability to process large volumes of data and produce reliable predictions. One critical health problem that can benefit from these approaches is malnutrition among toddlers, which continues to pose challenges to growth, development, and long-term well-being. This analysis aims to identify the most effective and efficient algorithms for classifying the nutritional status of toddlers based on anthropometric data. The review is grounded in relevant journal articles aligned with the research topic, which serve as the primary sources for synthesis. The selected studies underwent four stages of identification, selection, evaluation, and analysis to ensure both credibility and reliability. The analysis focuses on three main aspects: dataset characteristics, algorithms applied, and outcomes reported. Based on algorithm usage, three implementation strategies were identified: single model, multi-model, and model combination. The overall findings reveal that studies utilizing datasets with fewer than 500 records can effectively apply algorithms such as Random Forest, Decision Tree, and Naïve Bayes Classifier, which consistently achieve accuracy rates above 90%. For datasets exceeding 10,000 records, the XGBoost algorithm is recommended due to its scalability and efficiency in handling large-scale data. For datasets ranging between 500 and 10,000 records, hybrid approaches such as the C4.5 algorithm combined with Particle Swarm Optimization are preferable, with previous studies demonstrating an accuracy of 94.49%. This review highlights that algorithm selection should be adjusted according to dataset size and clinical needs. The findings provide valuable insights to support researchers, practitioners, and policymakers in developing accurate and effective solutions for toddler nutrition assessment