Towards the Early Diagnosis of Alzheimer's Disease via a Multicriteria Classification Model

Authors:
Amaury T. Brasil Filho;Plácido R. Pinheiro;André L. Coelho
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
Venue:
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Year:
2009

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Abstract

The very early detection of Alzheimer's disease (AD) has been deeply investigated in numerous studies in the past years. These studies have demonstrated that the pathology usually arises decades before the clinical diagnosis is effectively made, and so a reliable identification of AD in its earliest stages is one of the major challenges clinicians and researchers face nowadays. In the present study, we introduce a new approach developed upon a specific Multicriteria Decision Aid (MCDA) classification method to assist in the early AD diagnosis process. The MCDA method is centered on the concept of prototypes, that is, alternatives that serve as class representatives related to a given problem, and has its performance index very dependent upon the choice of values of some control parameters. In such regard, 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. Moreover, a new database has been designed taking as reference both the functional and cognitive recommendations of the Scientific Department of Cognitive Neurology and Aging 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). Various experiments have been performed over this database in a manner as to either fine-tune the components of the MCDA model or to compare its performance level with that exhibited by other state-of-the-art classification algorithms.