Designing of a New Intuitionistic Fuzzy Based Diabetic Diagnostic System

Designing of a New Intuitionistic Fuzzy Based Diabetic Diagnostic System

Supriya Raheja (The NorthCap University, Gurgaon, India) and Vaishali Jain (Development Department, Gaussian Network, Gurgaon, India)
Copyright: © 2018 |Pages: 14
DOI: 10.4018/IJFSA.2018010103
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Abstract

In this article, fuzzy set theory and other generalized theories are described as having extended the concept of classical set theory to uncertain problems. Fuzzy set theory plays a pivot role in the field of medical sciences to handle the vagueness and impreciseness of the data but no one has explored the field with vague set theory. With this objective, the authors propose a new diabetes diagnosis system called Intuitionistic Fuzzy based Diabetes Diagnostic System (IDDS). IDDS handles the imprecise data of system and helps to predict whether a person is diabetic or not using the concept of Intuitionistic Fuzzy set theory. IDSS uses the Intuitionistic-Fuzzification mechanism which works in two phases. In first phase, it converts the crisp data into intuitionistic fuzzy data and in second phase it generates the fuzzy value for each Intuitionistic value. IDDS has been implemented using MATLAB. The performance of IDDS is evaluated and compared with the Fuzzy Logic Diabetes Diagnosis System (FLDDS). The results prove that IDDS has better performance over FLDDS and it provides more accurate results than FLDDS.
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Diabetes Mellitus commonly known as Diabetes is a hormonal disorder mainly characterized by sugar level in the blood. The problem begins with poor metabolism. When body metabolism rate is poor and the quantity of insulin produced is inadequate, the body cells are unable to take in the glucose, thus raising glucose levels above normal. In this type of disease, immense increase of sugar level in blood owed to grouping of metabolic diseases. Due to this patient often complains of frequent urination, increased hunger, and increased thirst.

Diabetes can be either genetic or caused due to unhealthy lifestyle. However, exact causes may be discovered with proper medical tests. It is found in people of all age groups. So, there is a need of such mechanism that can diagnose the diabetes at its early stage with minimum cost and time without compromise with accuracy rate. Fuzzy set theory is one of the ways that can diagnose the diabetes using the expert’s knowledge stored in their database. This technique can diagnose the diabetes at its early stage by considering some important input parameters and it incur less cost.

Klaus-Peter Adlassnig (2001) demonstrated the importance and role of knowledge base decision support fuzzy system in medicine. The author has proved that fuzzy set theory and its derived theories are broadly suitable in the field of medicine. Lee and Wang (2007) presented a paper on the intelligent healthcare agent based on ontology. The paper presents an intelligent healthcare agent based on ontology to assist the medical staff in the understanding the meaning of the graph readings obtained from ventilators and for the recognition of respiratory waveform.

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