Abstract
This work presents a hybrid model, combining Bayesian Networks and the Multicriteria Method, for aiding in decision making for the neuropsychological diagnosis of Alzheimer’s disease. Due to the increase in life expectancy there is higher incidence of dementias. Alzheimer’s disease is the most common dementia (alone or together with other dementias), accounting for 50% of the cases. Because of this and due to limitations in treatment at late stages of the disease early neuropsychological diagnosis is fundamental because it improves quality of life for patients and theirs families. Bayesian Networks are implemented using NETICA tool. Next, the judgment matrixes are constructed to obtain cardinal value scales which are implemented through MACBETH Multicriteria Methodology. The modeling and evaluation processes were carried out with the aid of a health specialist, bibliographic data and through of neuropsychological battery of standardized assessments.
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Araújo de Castro, A.K., Pinheiro, P.R., Pinheiro, M.C.D. (2008). A Hybrid Model for Aiding in Decision Making for the Neuropsychological Diagnosis of Alzheimer’s Disease. In: Chan, CC., Grzymala-Busse, J.W., Ziarko, W.P. (eds) Rough Sets and Current Trends in Computing. RSCTC 2008. Lecture Notes in Computer Science(), vol 5306. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88425-5_51
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DOI: https://doi.org/10.1007/978-3-540-88425-5_51
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