| dc.description.abstract | The global prevalence of non-alcoholic fatty liver disease
(NAFLD) driven by unhealthy dietary habits and genetic factors is
closely associated with obesity, diabetes, and high cholesterol and
can progress to life-threatening cirrhosis. This presents a significant
health challenge, especially as no approvedmedications or targeted
treatments currently exist for NAFLD, emphasizing the critical role
of diet and exercise in managing thedisease. This paper presents a
comprehensive review combiningboth literature analysis and expert
consultation to explore advanced methodologies for developing a
food recommendation system tailored to liver disease patients.
Approaches such as Machine Learning (ML), Deep Learning (DL),
and Ontology-based AI were systematically evaluated, with the
ontology-based approach identified as the most effective. Insights
from the literature were confirmed through expert consultation, high-
high-lighting parameters like BMI, blood sugar levels, disease stage,
and food preferences are closely related to liver disease and crucial
for providing personalized dietary recommendations. The review
also highlights limitations in existing systems suchas inadequate
expert knowledge integration and insufficient attention to individual
dietary needs. Future work aims to develop a comprehensive
ontology-based food recommendation system, leveraging insights
from both the literature and expert consultation to improve patient
outcomes and quality of life | en_US |
