An automated system to diagnose the severity of adult depression

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

doi:10.1109/EAIT.2011.17

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 proceeding › Conference contribution

15 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 language	English
Title of host publication	Proceedings - 2nd International Conference on Emerging Applications of Information Technology, EAIT 2011
Pages	121-124
Number of pages	4
DOIs	https://doi.org/10.1109/EAIT.2011.17
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
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

Access to Document

10.1109/EAIT.2011.17

Cite this

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title = "An automated system to diagnose the severity of adult depression",

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%).",

author = "Subhagata Chattopadhyay and Fethi Rabhi and Preetisha Kaur and {Rajendra Acharya}, U.",

year = "2011",

month = apr,

day = "18",

doi = "10.1109/EAIT.2011.17",

language = "English",

isbn = "9780769543291",

pages = "121--124",

booktitle = "Proceedings - 2nd International Conference on Emerging Applications of Information Technology, EAIT 2011",

note = "2nd International Conference on Emerging Applications of Information Technology, EAIT 2011 ; Conference date: 19-02-2011 Through 20-02-2011",

}

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., 5734931, pp. 121-124, 2nd International Conference on Emerging Applications of Information Technology, EAIT 2011, Kolkata, India, 19-02-11. https://doi.org/10.1109/EAIT.2011.17

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T1 - An automated system to diagnose the severity of adult depression

AU - Chattopadhyay, Subhagata

AU - Rabhi, Fethi

AU - Kaur, Preetisha

AU - Rajendra Acharya, U.

PY - 2011/4/18

Y1 - 2011/4/18

N2 - 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%).

AB - 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%).

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BT - Proceedings - 2nd International Conference on Emerging Applications of Information Technology, EAIT 2011

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An automated system to diagnose the severity of adult depression

Abstract

Conference

All Science Journal Classification (ASJC) codes

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