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Abstract #1517

Machine learning classifiers on resting-state cerebrovascular reactivity in preclinical Alzheimer's disease

Kaio Felippe Secchinato1, Pedro Henrique Rodrigues da Silva1, Júlia Palaretti1, and Renata Ferranti Leoni1
1Departamento de Física, University of São Paulo, Ribeirão Preto, Brazil

Early detection of Alzheimer's disease (AD) increases the treatment benefits. However, it is still a challenging question which biomarkers are useful for early diagnosis. Then, we aimed to classify cognitively normal elderly regarding the possibility to develop AD based on resting-state cerebral vasoreactivity (CVR) values and neuropsychological (NP) scores. We used supervised machine learning algorithms. Our results suggest that Random Forest and K-Nearest Neighbors classifiers trained with CVR values of the vermis.7 (part of the cerebellum), and left parahippocampal gyrus, and Mini-Mental State Examination (MMSE), and Trail Making Test A scores can be useful on the early detection of AD.

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