Personalized Lifestyle Recommendations for Chronic Kidney Disease Patients Based on Individual Test Results

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.