Abstract:
The need for specialized strategies to improve health outcomes and the progression of
chronic illnesses have made personalized nutritional recommendation generation a very
important issue. Traditional techniques usually ignore individual variations in nutritional
needs and preferences in favor of generic recommendations and statistics from the
general population. We describe a knowledge graph-driven methodology for formulating
individualized dietary recommendations. Our approach makes use of knowledge graphs
to combine and analyze the complex data important to a person's health, lifestyle, and
eating habits. By employing this process, we can overcome the limitations of generic
guidelines and provide highly individualized assistance. We describe the multi-step
method used by our method, which incorporates data collection, curation, knowledge
graph development, and creation of customized recommendations. Through a complete
case study, we evaluate the effectiveness of our method by generating customized dietary
tips for a sample individual primarily based totally on their health and nutritional goals.
The results show the potential of knowledge graph-driven methodologies in increasing
the accuracy and relevance of nutritional recommendations. Furthermore, we discuss
future directions, which includes increasing the capabilities of knowledge graphs and
incorporating advanced technologies, to further increase the personalization and
effectiveness of nutritional recommendations in chronic disease management.
