Feature selection for a cooperative coevolutionary classifier in liver fibrosis diagnosis

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.