title: An associative memory approach to medical decision support systems.
abstract: Classification is one of the key issues in medical diagnosis. In this paper, a novel approach to perform pattern classification tasks is presented. This model is called Associative Memory based Classifier (AMBC). Throughout the experimental phase, the proposed algorithm is applied to help diagnose diseases; particularly, it is applied in the diagnosis of seven different problems in the medical field. The performance of the proposed model is validated by comparing classification accuracy of AMBC against the performance achieved by other twenty well known algorithms. Experimental results have shown that AMBC achieved the best performance in three of the seven pattern classification problems in the medical field. Similarly, it should be noted that our proposal achieved the best classification accuracy averaged over all datasets.
authors.affiliation: Center for Computing Research, CIC-IPN Building, Nueva Industrial Vallejo, GA Madero, Mexico City 07738, Mexico. maldape@ieee.org
authors.forename: Mario
authors.initials: M
authors.lastname: Aldape-Perez
authors.forename: Cornelio
authors.initials: C
authors.lastname: Yanez-Marquez
authors.forename: Oscar
authors.initials: O
authors.lastname: Camacho-Nieto
authors.forename: Amadeo
authors.initials: A
authors.lastname: J Arguelles-Cruz
mesh_terms: D000465:Algorithms; D020000:Decision Support Systems, Clinical; D004194:Disease / Q000145:classification*; D006801:Humans; D008568:Memory; D008800:Mexico