hh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Predicting mental illness at workplace using machine learning
Halmstad University, School of Information Technology.ORCID iD: 0000-0003-0878-8130
Halmstad University, School of Information Technology.ORCID iD: 0000-0001-7713-8292
2023 (English)In: Mehran University Research Journal of Engineering and Technology, ISSN 0254-7821, E-ISSN 2413-7219, Vol. 42, no 1, p. 95-108Article in journal (Refereed) Published
Abstract [en]

Mental illness (MI) is a leading cause of workplace absenteeism that often goes unrecognized and untreated. This paper presents a machine learning algorithm for predicting MI at workplace. The dataset consisted of responses from 1259 subjects collected through an online survey using a self-assessed questionnaire on the workplace environment. The responses were used as features for training a support vector machine to predict MI. Statistical analysis using the Guttmann correlation and the analysis of variance was done to determine feature significance. Results using 10-fold cross-validation showed that the model predicted MI with good accuracy. Findings support the feasibility of this approach for MI monitoring at the workplace as it offers an advantage over other technologies e.g., MRI scans, and EEG analysis, previously developed for the objective assessment of MI. © Mehran University of Engineering and Technology 2023

Place, publisher, year, edition, pages
Jamshoro: Mehran University of Engineering and Technology , 2023. Vol. 42, no 1, p. 95-108
Keywords [en]
Mental Illness, Support Vector Machine, Classification, Attention Deficit Disorder, Machine Learning
National Category
Clinical Medicine
Identifiers
URN: urn:nbn:se:hh:diva-52882DOI: 10.22581/muet1982.2301.10ISI: 001156983200010OAI: oai:DiVA.org:hh-52882DiVA, id: diva2:1844030
Available from: 2024-03-12 Created: 2024-03-12 Last updated: 2024-03-12Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records

Khan, TahaDougherty, Mark

Search in DiVA

By author/editor
Khan, TahaDougherty, Mark
By organisation
School of Information Technology
In the same journal
Mehran University Research Journal of Engineering and Technology
Clinical Medicine

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 28 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf