Feature selection for the classification of Alzheimer's disease data

Hany Alashwal, Areeg Abdalla, Mohamed El Halaby, Ahmed A. Moustafa

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

Abstract

In this paper, we describe the features of our large dataset (6400+ rows and 400+ features) that includes Alzheimer's disease (AD) patients, individuals with mild cognitive impairment (MCI, prodromal stage of Alzheimer's disease), and healthy individuals (without AD or MCI). We also, present a feature selection method applied on the dataset. Unlike prior data mining models that were applied to AD, our dataset is big in nature and includes genetic, neural, nutritional, and cognitive measures of all the individuals. All of these measures in the data have been shown by empirical studies to be related to the development of AD. We used a random forest classifier to discover which features best classify and differentiate between AD patients and healthy individuals. Identifying these features will likely provide evidence for protective factors against the development of AD.

Original languageEnglish
Title of host publicationProceedings of the 2020 3rd International Conference on Software Engineering and Information Management, ICSIM 2020 - Workshop 2020 the 3rd International Conference on Big Data and Smart Computing, ICBDSC 2020
PublisherAssociation for Computing Machinery
Pages41-45
Number of pages5
ISBN (Electronic)9781450376907
DOIs
Publication statusPublished - Jan 12 2020
Event3rd International Conference on Software Engineering and Information Management, ICSIM 2020 - and its Workshop 2020 the 3rd International Conference on Big Data and Smart Computing, ICBDSC 2020 - Sydney, Australia
Duration: Jan 12 2020Jan 15 2020

Publication series

NameACM International Conference Proceeding Series

Conference

Conference3rd International Conference on Software Engineering and Information Management, ICSIM 2020 - and its Workshop 2020 the 3rd International Conference on Big Data and Smart Computing, ICBDSC 2020
Country/TerritoryAustralia
CitySydney
Period1/12/201/15/20

Keywords

  • Alzheimer's Disease
  • Classification
  • Feature Selection
  • Mild Cognitive Impairment

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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