Using FAHP and Type II Fuzzy methods to prioritize the top ten factors in deciding to recommend diet in dyslipidemia patients

Introduction: According to experts, the decision about which diet to prescribe to patients withdyslipidemia depends on various factors. Some pay more attention to genetic and physical factors,others to food or lifestyle, and some pay attention to paraclinical lab test results or harmful habits.This research helps doctors to choose the most important ۱۰ factors to decide the recommendationof the best diet.Materials and methods: In a cross-sectional study with the aim of identifying such factors, basedon reliable sources and discussions with two nutritionists, a list of factors related to dyslipidemiawas prepared and presented in the form of a self-administered questionnaire to internists andnutritionists in three universities. The measurement of people’s opinion about factors was madeusing a visual analogue scale (VAS). The data was analyzed using Excel and MATLAB softwareand Fuzzification and Alpha Cut Set methods and fuzzy triangular membership function. Thedefinite values obtained from AHP were replaced with interval values obtained from FAHP andthe weight of each factor mentioned in the questionnaire was calculated separately and as a group.Finally, due to the difference between the raw numbers and the given weight, the “fuzzy type II”method was also compared with the previous methods and the outputs were put together.Findings: The most important factors, are body mass index (BMI), serum LDL level, daily eatingpattern, serum cholesterol level, uncontrolled diabetes, serum HDL level, type of fat consumed inthe diet, serum VLDL level, inappropriate eating habits of fat, and serum triglyceride level.Discussion:The results of the comparison of the three methods of raw numbers, FAHP and fuzzytype I and type II show that some factors such as smoking, physical activity level, patient age,stress, alcohol consumption and even chronic liver and kidney diseases do not play a significantrole in decision making. Therefore, the results of this research can be used as a guide for counselorsin determining and recommending the diet for dyslipidemia patients.

Keywords:

Fuzzy , Nutrition , Dyslipidemia , Medical Informatics , Soft Computing , Clinical Decision Making