DocumentCode :
2456375
Title :
Improvement in prediction rate and accuracy of diabetic diagnosis system using fuzzy logic hybrid combination
Author :
Undre, Poonam ; Kaur, Harjeet ; Patil, Prakash
Author_Institution :
Indira Coll. of Eng. & Manage., Pune, India
fYear :
2015
fDate :
8-10 Jan. 2015
Firstpage :
1
Lastpage :
4
Abstract :
Nowadays diabetes mellitus has become the major health problem among the people of all ages. The main problem in this type of dieses is its prediction. It is found that if diabetes mellitus is detected at early stages then it can be cured. So early detection of diabetes mellitus is important. There are different techniques with the help of which early detection of diabetes mellitus is possible. In this paper combination of three different methods used for early detection of diabetes mellitus are given. These three methods are fuzzy system, neural network, case based reasoning. By using combination of all these approaches, it is found that detection of diabetes mellitus at early stages is possible. Benefit of using these systems is that accuracy of prediction rate is higher as compared to other techniques.
Keywords :
case-based reasoning; diseases; fuzzy logic; fuzzy systems; medical diagnostic computing; neural nets; case based reasoning; diabetes mellitus early detection; diabetic diagnosis system accuracy; fuzzy logic hybrid combination; fuzzy system; neural network; prediction rate improvement; Artificial neural networks; Cognition; Diabetes; Diseases; Fuzzy logic; Fuzzy sets; Diabetes mellitus; case based reasoning; early detection; fuzzy system; neural network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pervasive Computing (ICPC), 2015 International Conference on
Conference_Location :
Pune
Type :
conf
DOI :
10.1109/PERVASIVE.2015.7087029
Filename :
7087029
Link To Document :
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