Intelligent System for
Early Detection of
Alzheimer's disease using
Domingo López Rodríguez
Ricardo de Abajo llamero
Antonio García Linares
The diagnosis of Alzheimer's disease (AD) due to its
evolution, occurs when neurological damage is
present and is irreversible. The goal is to develop
and implement an automated system for early
detection of AD, by processing neuroimaging, and
construction of automated and objective tools based
in Artificial Intelligence and Data Mining.
Age range: from 18 to 96. MCI and AD were present in some subjects older than 55.
Images were procedent from available MRI databases after passing a check to ensure
the necessary quality
Morphometric processing of these images was
carried out using standard methodologies and
packages such as SPM or FSL, besides our own
developments. The results of this processing fed
Computational Intelligence systems such as
decision trees, support vector machines and
genetic algorithms, apart from artificial neural
networks, to develop a system to classify the
state of the AD by neuroimaging.
Positive Predictive Value
Negative Predictive Value
To avoid over-training of the model, 10-fold cross validation was used.
The resulting model incorporated SVMs, GGAA and Decision Trees.
We have developed a computer system that is
neuroimaging studies, and with great accuracy,
if the subject is in a normal state or have any
chance of developing AD. It's a tool with great
potential for application in early diagnosis of AD.
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