In this exploration, we adopted a ranking approach based on tetradecagonal
fuzzy numbers, which are 14-sided polygons through which transit patterns such as
demand, supply, and transportation value are represented. Vogel’s Approximation
Method (VAM), a traditional method in fuzzy studies, is used to solve fuzzy
transportation problems to obtain fuzzy nominal results. Tetradecagonal fuzzy values
suggest an excellent nutrient rate for a fruit diet at a low cost. The fruit diet will
provide high-energy and nutritionally essential food for healthcare. This chapter
indicates compelling growth in the use of fuzzy logic in healthcare NLP. The nominal
result offers a constructive explanation that focuses on the data's imprecision while
providing an initial solution. Our path is constructive in amending fruit diets. By
assimilating tetradecagonal fuzzy numbers, which are 14-sided polygons, we can
accurately model the costs and nutritional values of various fruits, ensuring that the diet
remains within budget and provides high energy and essential nutrients. The ranking
schema in fuzzy transportation for healthcare in NLP is adequately commensurate with
cost moderation and health supplement requirements, making it feasible to map a wellbalanced fruit that increases health benefits for healthcare NLP. Ranking
tetradecagonal fuzzy numbers allows us to exhibit compelling upgrades in the
application of fuzzy logic to transportation and dietary optimization, offering both
practical and nutritional advantages.
Keywords: Fuzzy transportation problem, Fuzzy ranking approach, Healthcare data NLP, Macronutrients, Micronutrients, Nutritionally balanced diet, Tetradecagonal fuzzy number.
