This paper presents an automatic tool capable to learn from a patients data set 
with 24 medical indicators characterizing each sample and to subsequently use 
the acquired knowledge to differentiate between five degrees of liver fibrosis. 
The indicators represent clinical observations and the liver stiffness provided 
by the new, non-invasive procedure of Fibroscan. The proposed technique combines 
a hill climbing algorithm that selects subsets of important attributes for an 
accurate classification and a core represented by a cooperative coevolutionary 
classifier that builds rules for establishing the diagnosis for every new patient. 
The results of the novel method proved to be superior as compared to the ones 
obtained by other important classification techniques from the literature. 
Additionally, the proposed methodology extracts a set of the most meaningful 
attributes from the available ones.