AMIA Annu Symp Proc - Automatic Prediction of Conversion from Mild Cognitive Impairment to Probable Alzheimer's Disease using Structural Magnetic Resonance Imaging.

Tópicos

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Resumo

Mild Cognitive Impairment (MCI) is thought to be a precursor to the development of early Alzheimer's disease (AD). For early diagnosis of AD, the development of a model that is able to predict the conversion of amnestic MCI to AD is challenging. Using automatic whole-brain MRI analysis techniques and pattern classification methods, we developed a model to differentiate AD from healthy controls (HC), and then applied it to the prediction of MCI conversion to AD. Classification was performed using support vector machines (SVMs) together with a SVM-based feature selection method, which selected a set of most discriminating predictors for optimizing prediction accuracy. We obtained 90.5% cross-validation accuracy for classifying AD and HC, and 72.3% accuracy for predicting MCI conversion to AD. These analyses suggest that a classifier trained to separate HC vs. AD has substantial potential for predicting MCI conversion to AD.

Resumo Limpo

mild cognit impair mci thought precursor develop earli alzheim diseas ad earli diagnosi ad develop model abl predict convers amnest mci ad challeng use automat wholebrain mri analysi techniqu pattern classif method develop model differenti ad healthi control hc appli predict mci convers ad classif perform use support vector machin svms togeth svmbase featur select method select set discrimin predictor optim predict accuraci obtain crossvalid accuraci classifi ad hc accuraci predict mci convers ad analys suggest classifi train separ hc vs ad substanti potenti predict mci convers ad

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