An Optimized Intuitionistic Fuzzy Associative Memories (OIFAM) to Identify the Complications of Type 2 Diabetes Mellitus (T2DM)

Fuzzy associative memories (FAM) is a recurrent neural network, consisting of two layers. Since points of the fuzzy set are defined in a cube, it maps between cubes. That is, it maps from input fuzzy set into an output fuzzy set. While the input layer is deliberated as the cause infusing agent the output layer influences the requisite effect. It is a powerful technique to analyze the cause and effect of any problem. Determining the most influential factors in the cause and effect group of any problem is a challenging task. To quench such a task, this present study constructs an optimized intuitionistic fuzzy associative memory using an intuitionistic fuzzy set and a variance of fitness formula. To check the validity of the proposed model, Type 2 diabetes mellitus is taken for diagnosing the early complications of T2DM patients from the risk factors.