A unified granular fuzzy-neuro min-max relational framework for medical diagnosis

摘要

We propose to accommodate herein our novel unified granular framework that uses a developed hybrid fuzzy-neuro relational system in order to tackle a complex medical diagnosis problem and to understand the influence of syndromes in relation to symptoms. To this goal, we propose to adapt our novel computational granular unified framework that is cognitively-motivated for learning IF-THEN fuzzy weighted diagnosis rules by using a hybrid neuro-fuzzy or fuzzy-neuro possibilistic model appropriately crafted as a means to automatically extract or learn diagnosis rules from only input-output examples by integrating some useful concepts from the human cognitive processes and adding some interesting granular functionalities. This learning scheme uses an exhaustive search over the fuzzy partitions of involved variables, automatic fuzzy hypotheses generation, formulation and testing, and approximation procedure of min-max relational equations. The main idea is to start learning from coarse fuzzy partitions of the involved proteins variations input variables and proceed progressively toward fine-grained partitions until finding the appropriate partitions that fit the data. According to the complexity of the problem at hand, it learns the whole structure of the fuzzy system, i.e., conjointly appropriate fuzzy partitions, appropriate fuzzy diagnosis rules, their number and their associated trapezoidal membership functions.