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P2‐096: COMBINING STRUCTURAL MRI, PROTEOMIC, AND GENETIC ADNI DATA FOR EARLY DETECTION OF ALZHEIMER'S DISEASE VIA RANDOM FOREST CLASSIFIERS
Author(s) -
Casanova Ramon,
Hsu FangChi,
Neth Bryan J.,
Sink Kaycee M.,
Rapp Stephen,
Williamson Jeff,
Espeland Mark A.,
Craft Suzanne
Publication year - 2014
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2014.05.770
Subject(s) - random forest , alzheimer's disease neuroimaging initiative , artificial intelligence , computer science , apolipoprotein e , cognitive impairment , machine learning , pattern recognition (psychology) , medicine , disease
Data presented as mean (SD) or relative frequencies; HC: Healthy controls; DC: Diseased controls; AD: Dementia due to Alzheimer’s disease; CSF b-amyloid 1-42 values indicative of AD: b-amyloid 1-42 levels in cerebrospinal fluid (CSF) 642 ng/l or 192 pg/ml for themonocentric and multicentric dataset respectively; CSF p-tau values indicative of AD: tau phosphorylated at threonine 181 levels in cerebrospinal fluid (CSF) 61 ng/l or 192 pg/ml for the monocentric and multicentric dataset respectively; CSF t-tau values indicative of AD: total tau levels in cerebrospinal fluid (CSF) 252 ng/l or 94 pg/ml for the monocentric and multicentric dataset respectively. Poster Presentations: P2 P506
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