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Abstract #3517

Brain atrophy and machine learning algorithms on the prediction of dementia development

Pedro Henrique Rodrigues da Silva1, Kaio Felippe Secchinato1, Julia Palaretti1, and Renata Ferranti Leoni1
1USP, Ribeirão Preto, Brazil

A challenging issue regarding the early diagnosis of the Alzheimer's disease (AD) is the selection of biomarkers. In this study, we aimed to classify cognitively normal elderly regarding the possibility to develop AD based on brain atrophy and neuropsychological scores, and using supervised machine learning algorithms. Our results suggest Naïve-Bayes (NB) classifiers with left postcentral and left middle temporal cortical thickness or right lateral ventricle, right inferior parietal and Corpus Callosum (CC) Mid Posterior volumes can be useful to identify in the early stage the subjects with higher risks to develop AD.

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