REVIEW AND ANALYSIS OF ARTIFICIAL INTELLIGENCE AND ASSOCIATION RULES IN NUTRITION MONITORING SYSTEMS
DOI:
https://doi.org/10.47390/ts-v4i2y2026N02Keywords:
artificial intelligence, dietary monitoring, personalized diet, associative rules, Apriori algorithm, FP-Growth algorithm, intelligent data analysisAbstract
This article examines and analyzes artificial intelligence technologies and associative rule mining in dietary monitoring systems. Intelligent analysis of large volumes of dietary data enables the development of personalized recommendations for healthy eating. The article discusses the general model of dietary monitoring systems, the role of artificial intelligence in individual nutrition, and the theoretical foundations of associative rule mining algorithms — Apriori and FP-Growth. Additionally, the advantages and practical aspects of forming an individualized diet based on the integration of artificial intelligence and associative rules are highlighted. The research results demonstrate the effectiveness of using intelligent systems to ensure healthy nutrition and prevent diet-related diseases.
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