Domingo López RodríguezRicardo de Abajo llameroAntonio García Linares

Intelligent System for Early Detection of

Alzheimer's disease using neuroimaging

Intelligent System for Early Detection of

Alzheimer's disease using neuroimaging

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.

MEN WOMEN TOTAL

HEALTHY 694 493 1187

MCI 348 434 782

AD 55 76 131

TOTAL 1097 1003 2100

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.

Parameter Value

Correct Classification 91,48%

Sensitivity 90,80%

Specificity 92,30%

Positive Predictive Value 0,886

Negative Predictive Value 0,939

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 able to classify, based on structural 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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