Bayesian and profile likelihood change point methods for modeling cognitive function over time
Computational Statistics & Data Analysis
Applying a decision making model in the early diagnosis of Alzheimer's disease
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Comparison of Two MCDA Classification Methods over the Diagnosis of Alzheimer's Disease
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
ICICA'10 Proceedings of the First international conference on Information computing and applications
Towards the applied hybrid model in decision making: support the early diagnosis of type 2 diabetes
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
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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.