Abstract
Diabetes has become a major concern nowadays and its complications are affecting various organs of a diabetic patient. Therefore, a multi-dimensional technique including all parameters is required to detect the cause, its proper diagnostic procedure and its prevention. In this present work, a technique has been introduced that seeks to build an implementation for the intelligence system based on neural networks. Moreover, it has been described that how the proposed technique can be used to determine the membership together with the non-membership functions in the intuitionistic environment. The dataset has been obtained from Pima Indians Diabetes Database (PIDD). In this work, a complete diagnostic procedure of diabetes has been introduced with seven layered structural frameworks of an Intuitionistic Neuro Sugeno Fuzzy System (INSFS). The first layer is the input, in which six factors have been taken as an input variable. Subsequently, a neural network framework has been developed by constructing IFN for all the six input variables, and then this input has been fuzzified by using triangular intuitionistic fuzzy numbers. In this work, we have introduced a novel optimization technique for the parameters involved in the INSFS. Moreover, an inference system has also been framed for the neural network known as INFS. The results have also been given in the form of tables, which describe each concluding factor.
Highlights
A survey was given by Viharos and Kis on NFS in 2015 [19], this study shows the utility of both the system, i.e., fuzzy inference system and neural network. [20] gave a prediction fuzzy model for identification and prevention of diabetes, this study expected the five major complications which have arisen due to diabetes
A genetic algorithm has been used in the situation of SelfOrganized Fuzzy Neural Networks [36], optimized weight technique of artificial neural networks [37], the type-2 fuzzy logic system which is used for the linguistic prognostic models [38] and Elman model based neural network algorithm [39]
The entire work done in this article illustrates the following points: 1) Proposed intuitionistic fuzzy logic-based neuro system shows the diagnostic process of diabetes with the help of membership and non-membership function with some hesitation margins
Summary
After Hájek, P. and Olej, V., Zhao, J. et al proposed “a general fuzzy cerebella model neural network multidimensional classifier using Intuitionistic fuzzy sets for medical identification” [29]. The basic objectives of this research paper are pointed out the following points to focus the whole work as follows: 1) We will design a novel intuitionistic fuzzy logic based neural network approach for the diagnosis of diabetes. 2) We observed six input factors and with the help of the fuzzification process; we constructed the membership function for these inputs and with the help of Sugeno’s fuzzy inference system We applied this over the intuitionistic fuzzy numbers.
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