The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2016.070609
PDF

Assessment for the Model Predicting of the Cognitive and Language Ability in the Mild Dementia by the Method of Data-Mining Technique

Author 1: Haewon Byeon
Author 2: Dongwoo Lee
Author 3: Sunghyoun Cho

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 7 Issue 6, 2016.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Assessments of cognitive and verbal functions are widely used as screening tests to detect early dementia. This study developed an early dementia prediction model for Korean elderly based on random forest algorithm and compared its results and precision with those of logistic regression model and decision tree model. Subjects of the study were 418 elderly (135 males and 283 females) over the age of 60 in local communities. Outcome was defined as having dementia and explanatory variables included digit span forward, digit span backward, confrontational naming, Rey Complex Figure Test (RCFT) copy score, RCFT immediate recall, RCFT delayed recall, RCFT recognition true positive, RCFT recognition false positive, Seoul Verbal Learning Test (SVLT) immediate recall, SVLT delayed recall, SVLT recognition true positive, SVLT recognition false positive, Korean Color Word Stroop Test (K-CWST) color reading correct, and K-CWST color reading error. The Random Forests algorithm was used to develop prediction model and the result was compared with logistic regression model and decision tree based on chi-square automatic interaction detector (CHAID). As the result of the study, the tests with high level of predictive power in the detection of early dementia were verbal memory, visuospatial memory, naming, visuospatial functions, and executive functions. In addition, the random forests model was more accurate than logistic regression and CHIAD. In order to effectively detect early dementia, development of screening test programs is required which are composed of tests with high predictive power

Keywords: random forests; data mining; mild dementia; risk factors; neuropsychological test

Haewon Byeon, Dongwoo Lee and Sunghyoun Cho, “Assessment for the Model Predicting of the Cognitive and Language Ability in the Mild Dementia by the Method of Data-Mining Technique” International Journal of Advanced Computer Science and Applications(IJACSA), 7(6), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070609

@article{Byeon2016,
title = {Assessment for the Model Predicting of the Cognitive and Language Ability in the Mild Dementia by the Method of Data-Mining Technique},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.070609},
url = {http://dx.doi.org/10.14569/IJACSA.2016.070609},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {6},
author = {Haewon Byeon and Dongwoo Lee and Sunghyoun Cho}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org