Longitudinal measurement and hierarchical classification framework for the prediction of Alzheimer's disease Academic Article uri icon

Overview

MeSH Major

  • Aged
  • Cognition
  • Geriatric Assessment
  • Neuropsychological Tests

abstract

  • Accurate prediction of Alzheimer's disease (AD) is important for the early diagnosis and treatment of this condition. Mild cognitive impairment (MCI) is an early stage of AD. Therefore, patients with MCI who are at high risk of fully developing AD should be identified to accurately predict AD. However, the relationship between brain images and AD is difficult to construct because of the complex characteristics of neuroimaging data. To address this problem, we present a longitudinal measurement of MCI brain images and a hierarchical classification method for AD prediction. Longitudinal images obtained from individuals with MCI were investigated to acquire important information on the longitudinal changes, which can be used to classify MCI subjects as either MCI conversion (MCIc) or MCI non-conversion (MCInc) individuals. Moreover, a hierarchical framework was introduced to the classifier to manage high feature dimensionality issues and incorporate spatial information for improving the prediction accuracy. The proposed method was evaluated using 131 patients with MCI (70 MCIc and 61 MCInc) based on MRI scans taken at different time points. Results showed that the proposed method achieved 79.4% accuracy for the classification of MCIc versus MCInc, thereby demonstrating very promising performance for AD prediction.

authors

publication date

  • January 12, 2017

Research

keywords

  • Academic Article

Identity

Language

  • eng

PubMed Central ID

  • PMC5227696

Digital Object Identifier (DOI)

  • 10.1038/srep39880

PubMed ID

  • 28079104

Additional Document Info

start page

  • 39880

volume

  • 7