Classification of mild cognitive impairment subtypes using neuropsychological data

Upul Senanayake, Arcot Sowmya, Laughlin Dawes, Nicole A. Kochan, Wei Wen, Perminder Sachdev

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

2 Citations (Scopus)

Abstract

While the research on Alzheimer's disease (AD) is progressing, timely intervention before an individual becomes demented is often emphasized. Mild Cognitive Impairment (MCI), which is thought of as a prodromal syndrome to AD, may be useful in this context as potential interventions can be applied to individuals at increased risk of developing dementia. The current study attempts to address this problem using a selection of machine learning algorithms to discriminate between cognitively normal individuals and MCI individuals among a cohort of community dwelling individuals aged 70-90 years based on neuropsychological test performance. The overall best algorithm in our experiments was AdaBoost with decision trees while random forests was consistently stable. Ten-fold cross validation was used with ten repetitions to reduce variability and assess generalizing capabilities of the trained models. The results presented are consistently of the same calibre or better than the limited number of similar studies reported in the literature.

Original languageEnglish
Title of host publicationICPRAM 2016
Subtitle of host publicationproceedings of the 5th International Conference on Pattern Recognition Applications and Methods
EditorsMaria de Marsico, Gabriella Sanniti di Baja, Ana Fred
Place of PublicationPortugal
PublisherSciTePress
Pages620-629
Number of pages10
ISBN (Electronic)9789897581731
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event5th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2016 - Rome, Italy
Duration: 24 Feb 201626 Feb 2016

Other

Other5th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2016
CountryItaly
CityRome
Period24/02/1626/02/16

Keywords

  • Alzheimer's disease
  • Machine learning
  • Mild cognitive impairment
  • Neuropsychological features

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