Masters Theses

Abstract

Depression is a serious mental health problem affecting the mind and body of people in our society. As of today, depression affects about 5 to 7% of the American population, which roughly translates to 14 million people! A particularly worrying concern these days is the increasing incidence of depression among college students. The problem is so severe that many US campuses have dedicated centers with mental health professionals catering to students needs. Although there are very effective treatments for depression, studies report that ~80% of depressed students do not seek any help due to the lack of perception of the problem, low self esteem, and loneliness.

The aim of this thesis is to develop a transparent depression identification/ classification framework that can be deployed in college settings. The premise of this thesis stems from surveys in the mental health community revealing extensive known correlations between Internet use and Depression. The thesis investigates the feasibility of identifying and classifying depression via mining real Internet usage patterns derived from Cisco Netflow data.

To the best of the authors knowledge, this thesis is the first to study depression using real Internet data. As a result, several new statistical results correlating Internet use with depression is presented. Additionally, this thesis takes a quantum leap forward in transparent depression detection by developing a classifier that can predict depression with ~74% accuracy, demonstrating the feasibility of the approach. The performance was further improved to ~84% by considering deviations in baseline Internet activity of a user.

The strength of the proposed framework lies in its practicality, extensibility, transparency, and privacy preserving nature"--Abstract, page iii.

Advisor(s)

Chellappan, Sriram

Committee Member(s)

Wunsch, Donald C.
Madria, Sanjay Kumar

Department(s)

Computer Science

Degree Name

M.S. in Computer Science

Publisher

Missouri University of Science and Technology

Publication Date

2011

Pagination

ix, 92 pages

Note about bibliography

Includes bibliographical references (pages 84-91).

Rights

© 2011 Raghavendra Kotikalapudi, All rights reserved.

Document Type

Thesis - Open Access

File Type

text

Language

English

Subject Headings

Depression, Mental -- DiagnosisCollege students -- PsychologyInternet users -- Statistics -- Data processing.Data mining

Thesis Number

T 10198

Print OCLC #

904049088

Electronic OCLC #

908106812

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