An automated system to diagnose the severity of adult depression

Subhagata Chattopadhyay, Fethi Rabhi, Preetisha Kaur, U. Rajendra Acharya

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

14 Citations (Scopus)

Abstract

Depression is a common but ominous psychological disorder that threatens one's quality of life. The screening and grading of depression is still a manual process and grades are often determined in ranges, e.g., 'mild to moderate' and 'moderate to severe' instead of making them more specific as 'mild', 'moderate', and 'severe'. Such grading is confusing and affects the management plan. Given this practical issue, the present paper attempts to differentiate depression grades more accurately using a Back Propagation Neural Network (BPNN) classifier, built in MATLAB. The overall accuracy of the classifier is 100% for mild, 77% for moderate and 90% for severe grades with a good model fit (R=94%).

Original languageEnglish
Title of host publicationProceedings - 2nd International Conference on Emerging Applications of Information Technology, EAIT 2011
Pages121-124
Number of pages4
DOIs
Publication statusPublished - 18-04-2011
Externally publishedYes
Event2nd International Conference on Emerging Applications of Information Technology, EAIT 2011 - Kolkata, India
Duration: 19-02-201120-02-2011

Conference

Conference2nd International Conference on Emerging Applications of Information Technology, EAIT 2011
CountryIndia
CityKolkata
Period19-02-1120-02-11

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems

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    Chattopadhyay, S., Rabhi, F., Kaur, P., & Rajendra Acharya, U. (2011). An automated system to diagnose the severity of adult depression. In Proceedings - 2nd International Conference on Emerging Applications of Information Technology, EAIT 2011 (pp. 121-124). [5734931] https://doi.org/10.1109/EAIT.2011.17