Intelligent System for Early Detection ofAlzheimers disease using neuroimaging Domingo López Rodríguez Ricardo de Abajo llamero Antonio García Linares
The diagnosis of Alzheimers disease (AD) due to itsevolution, occurs when neurological damage ispresent and is irreversible. The goal is to developand implement an automated system for earlydetection of AD, by processing neuroimaging, andconstruction of automated and objective tools basedin Artificial Intelligence and Data Mining.
MEN WOMEN TOTALHEALTHY 694 493 1187MCI 348 434 782AD 55 76 131TOTAL 1097 1003 2100Age 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 ensurethe 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 ValueCorrect Classification 91,48%Sensitivity 90,80%Specificity 92,30%Positive Predictive Value 0,886Negative Predictive Value 0,939To 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 isable to classify, based on structuralneuroimaging studies, and with great accuracy,if the subject is in a normal state or have anychance of developing AD. Its a tool with greatpotential for application in early diagnosis of AD.
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