Feature selection and time regression software: Application on predicting Alzheimer's disease progress

Dimitrios Ververidis, Mark van Gils, Juha Koikkalainen, Jyrki Lötjönen

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

3 Citations (Scopus)

Abstract

In this paper, the Bayes classifier is used to predict Alzheimer's disease progress. The classifier is trained on a subset of the Alzheimer's Disease Neuroimaging Initiative database. Subjects are diagnosed by doctors as belonging to healthy, mild-cognitive impaired, and Alzheimer's disease class. A software tool for features selection and time regression is developed. The tool utilizes a variant of the Sequential Forward Selection (SFS) algorithm for feature selection, where the criterion used for selecting features is the correct classification rate of the Bayes classifier. The tool also employs linear regression to predict future values of selected biomarkers, such as the hippocampus volume, from past measurements, so that future class of the subject can be predicted.

Original languageEnglish
Title of host publicationProceedings of EUSIPCO 2010
Pages1179-1183
Publication statusPublished - 1 Dec 2010
MoE publication typeNot Eligible
Event18th European Signal Processing Conference, EUSIPCO 2010 - Aalborg, Denmark
Duration: 23 Aug 201027 Aug 2010

Conference

Conference18th European Signal Processing Conference, EUSIPCO 2010
CountryDenmark
CityAalborg
Period23/08/1027/08/10

Keywords

  • feature selection
  • Alzheimer`s disease
  • pattern recognition
  • decision support
  • data mining

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