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International Journal of Emergency Mental Health and Human Resilience | ISSN: 1522-4821 | Volume 20

November 26-27, 2018 | Los Angeles, USA

Psychiatry, Mental Health Nursing and Healthcare

World Summit on

Applied Psychology, Psychiatry and Mental Health

International Conference on

Development and validation of predictive models for depression using patient health questionnaire-9 data

Jonathan C Huang

The Episcopal Academy, USA

epression, the leading cause of suicide worldwide, is a serious, widespread and growing mental health disorder that has now been

labeled a global health epidemic. The patient health questionnaire-9 (PHQ-9), a depression-screener questionnaire, has emerged

as an effective diagnostic tool globally. Using US PHQ-9 patient response data and corresponding demographic data from 2013-

2014 and 2015-2016, this study conducts a comprehensive big data analysis of the response data to develop and validate predictive

models for depression probability. Age at screening, gender, race/ethnicity, education level and body weight were proposed as factors

correlated with depression. Two models were constructed using RStudio to explore these correlations: a logistic regression model

and an artificial neural network. The logistic regression predictive model performed better than the artificial neural network in an

unfamiliar dataset, whereas the opposite was true in a familiar dataset. Both models supported that the proposed factors are indeed

significantly correlated with depression. The logistic regression model indicated that females and those with weight problems are

more likely to have depression and that the likelihood of depression increases with age, decreases with higher education levels and

varies by race. The artificial neural network indicated that age, the Asian race, some college education and weight problems are the

most significant factors affecting depression probability, in that order. Based on these results, populations most at-risk for depression

are identified and appropriate measures should be taken to combat depression.

Int J Emerg Ment Health, Volume 20

DOI: 10.4172/1522-4821-C5-024