Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  introduces a novel fuzzy model - transductive neural-fuzzy classifier with ighted data normalization (TWNFC). While inductive approaches are concerned with the development of a model to approximate data in the whole problem space (induction), and consecutively - using this model to calculate the output value(s) for a new input vector (deduction), in transductive systems a local model is developed for every new input vector, based on some closest data to this vector from the training data set. The ighted data normalization method (WDN) optimizes the data normalization ranges for the input variables of a system. A steepest descent algorithm is used for training the TWNFC model. The TWNFC is illustrated on a case study: a real medical decision support problem of estimating the survival of haemodialysis patients. This personalized modeling can also be applied to other distance-based, prototype learning neural network or fuzzy inference models
['Tian Min Ma', 'Qun Song', 'Marshall Mr', 'N. Kasabov']
I am writing a research paper on a idea TWNFC - Transductive Neural-Fuzzy Classifier with Weighted Data Normalization and Its Application in Medicine
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