Designing Fuzzy Algorithms to Develop Healthy Dietary Pattern

Background: fuzzy logic, a mathematical approach, defines the percentage of desirability for recommended amount of food groups and describes the range of intakes, from deficiency to excess. objectives: the purpose of this research was to find the best fuzzy dietary pattern that constraints energy and nutrients by the iterative algorithm. materials and methods: an index is derived that reflects how closely the diet of an individual meets all the nutrient requirements set by the dietary reference intake. fuzzy pyramid pattern was applied for the energy levels from 1000 to 4000 kcal which estimated the range of recommended servings for seven food groups including fruits, vegetables, grains, meats, milk, oils, fat and added sugar. results: the optimum (lower attention – upper attention) recommended servings per day for fruits, vegetables, grain, meat, dairy, and oils of the 2000 kcal diet were 4.06 (3.75-4.25), 6.69 (6.25-7.00), 5.69 (5.75-6.25), 4.94 (4.5-5.2), 2.75(2.50-3.00), and 2.56 (2.5-2.75), respectively. the fuzzy pattern met most recommended nutrient intake levels except for potassium and vitamin e, which were estimated at 98% and 69% of the dietary reference intake, respectively. conclusions: using fuzzy logic provides an elegant mathematical solution for finding the optimum point of food groups in dietary pattern.