Comparison of Two MCDA Classification Methods over the Diagnosis of Alzheimer's Disease

Authors:
Amaury T. Brasil Filho;Plácido Rogerio Pinheiro;André L. Coelho;Nathanael C. Costa
Affiliations:
Graduate Program in Applied Informatics, University of Fortaleza, Fortaleza CE, Brazil;Graduate Program in Applied Informatics, University of Fortaleza, Fortaleza CE, Brazil;Graduate Program in Applied Informatics, University of Fortaleza, Fortaleza CE, Brazil;Graduate Program in Applied Informatics, University of Fortaleza, Fortaleza CE, Brazil
Venue:
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Year:
2009

Citing 3
Cited 0

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A Hybrid Model for Aiding in Decision Making for the Neuropsychological Diagnosis of Alzheimer's Disease

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Abstract

In the present study, we introduce, compare and apply an approach developed upon two Multicriteria Decision Aid (MCDA) classification methods to assist clinicians and researchers in the diagnosis of Alzheimer's disease (AD). Trying to leverage the classifiers' performances, two techniques, one based on ELECTRE IV methodology and the other on a customized genetic algorithm, are employed in order to select the prototypes and calibrate the control parameters automatically. Various experiments were performed over a novel dataset that takes as reference both the functional and cognitive recommendations of the Brazilian Academy of Neurology and a neuropsychological battery of exams made available by the well-known Consortium to Establish a Registry for Alzheimer's Disease (CERAD).