Automatic Prediction of Conversion from Mild Cognitive Impairment to Probable Alzheimer's Disease using Structural Magnetic Resonance Imaging

Kwangsik Nho, Li Shen, Sungeun Kim, Shannon L. Risacher, John D. West, Tatiana Foroud, Clifford R Jr. Jack, Michael W. Weiner, Andrew J. Saykin

Research output: Contribution to journalArticle

17 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)542-546
Number of pages5
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
StatePublished - 2010
Externally publishedYes


ASJC Scopus subject areas

  • Medicine(all)

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