Title: A unified granular fuzzy-neuro min-max relational framework for medical diagnosis
Authors: Mokhtar Beldjehem
Addresses: Sainte-Anne's University, 1589 Walnut Street, Halifax, Nova Scotia, B3H 3S1, Canada
Abstract: 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.
Keywords: medical diagnosis; possibility theory; IF-THEN fuzzy weighted rules; hybrid granular fuzzy-neuro model; approximation; min-max relational equations; linguistic approximation; medical syndromes; medical symptoms; healthcare technology; fuzzy logic; neural networks; granular computing; modelling.
International Journal of Advanced Intelligence Paradigms, 2011 Vol.3 No.2, pp.122 - 144
Published online: 30 Sep 2014 *Full-text access for editors Access for subscribers Purchase this article Comment on this article