Classify the Nutritional Status of Iraqi children under Five Years Using Fuzzy Classification

Abstract

 In this paper, we applied the theory of Fuzzy Sets to classify the nutritional status of children under five years of age using fuzzy logic and two fuzzy classification methods (Mamdani and Sugeno). We relied on linguistic variables (weight and height) to categorize children into more accurately defined categories of nutritional status. The goal is to minimize the chances of misdiagnosis and provide precise treatment for each category, thus contributing to the creation of a more resilient society with high levels of health. In this study, we employed a sample of 16,487 Iraqi children under the age of five years, divided into 12 age categories.

The results, after calculating the classification accuracy criterion, showed that among the two methods compared to the classical classification, the Sugeno method proved to be the best, with an accuracy level of around 60% for various age categories for both males and females.