Diet & Diabetes: An Intuitionistic Fuzzy Multiobjective Model

Diabetes is one of the most common illnesses internists encounter and an emerging global health problem. This is because the lifestyle of people today is sedentary and lacks the nutrition required in their diet. To prevent this, we need an optimal diet model. Accordingly, to the experts, the low-calorie diet having high starch carbohydrates, high fiber, and low fat is the best way to treat diabetes. Consequently, the proposed model is a multiobjective linear programming model with the maximization of fiber and carbohydrates and the minimization of fat and sugar. The constraints are daily maximum to minimum nutrient requirements of carbohydrates, fat, fiber, and sugar. But in most studies, the daily nutrient intake decisions were made based on crisp data. By prescribing a diet based on crisp data, some of the realities are neglected. Furthermore, establishing a precise threshold for the upper and lower limits of individuals’ daily nutrient intake is challenging due to various factors including geographical location, gender, age, and the inherent variability and lack of clarity in nutrient use. The fuzzy concept can best represent obscurity. But the fuzzy concepts can represent only the chance of a healthy diet acceptance and what if the chance of diet is not healthy is non-acceptance. Hence Intuitionistic fuzzy sets are employed for the acceptance and non-acceptance of nutrients in a diabetic diet. So both the objective and set of constraints are intuitionistic fuzzy sets. A numerical example illustrates the diet model under an intuitionistic fuzzy environment. The findings show that the degree of acceptance of various daily nutrients in a diabetic diet is 97% and rejection 29% for the age group between 35 to 45 years.