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 language | English |
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Title of host publication | Proceedings - 2nd International Conference on Emerging Applications of Information Technology, EAIT 2011 |
Pages | 121-124 |
Number of pages | 4 |
DOIs | |
Publication status | Published - 18-04-2011 |
Externally published | Yes |
Event | 2nd International Conference on Emerging Applications of Information Technology, EAIT 2011 - Kolkata, India Duration: 19-02-2011 → 20-02-2011 |
Conference
Conference | 2nd International Conference on Emerging Applications of Information Technology, EAIT 2011 |
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Country/Territory | India |
City | Kolkata |
Period | 19-02-11 → 20-02-11 |
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Computer Science Applications
- Information Systems