Predicting Dementia Risk to Depressive Disorder Patients: A classification Approach

Hsiao Ting Tseng, Hsiao Chi Li, Chia Lun Lo, Tai Hsiang Shen, Shu Chiung Lin

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

Abstract

The WHO identified depressive disorder as one of the three major diseases in the 21st century and studies have shown that patients with depression are more likely than nondepression to have dementia in the future. However, although there are many related studies that point out that depressive disorder is one of the important factor of dementia, however, these findings are not consistent. In addition, there has been no study of evidence-based construction of dementia prediction model of depressive disorder patients for clinical practice. Therefore, this study will use supervised learning techniques to construct a follow-up dementia prediction model for depressive disorder patients to assist depressive disorder patients and their medical staffs to predict his/her possible risk of suffering from dementia, and then develop early intervention and prevention measures.

Original languageEnglish
Title of host publicationProceedings of 2019 International Conference on Machine Learning and Cybernetics, ICMLC 2019
PublisherIEEE Computer Society
ISBN (Electronic)9781728128160
DOIs
StatePublished - Jul 2019
Event18th International Conference on Machine Learning and Cybernetics, ICMLC 2019 - Kobe, Japan
Duration: 7 Jul 201910 Jul 2019

Publication series

NameProceedings - International Conference on Machine Learning and Cybernetics
Volume2019-July
ISSN (Print)2160-133X
ISSN (Electronic)2160-1348

Conference

Conference18th International Conference on Machine Learning and Cybernetics, ICMLC 2019
Country/TerritoryJapan
CityKobe
Period7/07/1910/07/19

Keywords

  • Dementia
  • Depressive Disorder
  • Disease Severity
  • Machine Learning

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