Detecting psychological symptom patterns using regularized multinomial logistic regression

Salih Tutun; Abdulaziz A. Ahmed; Sedat Irgil; Ilker Yesilkaya; Mohammad T. Khasawneh

Detecting psychological symptom patterns using regularized multinomial logistic regression

Salih Tutun, Abdulaziz A. Ahmed, Sedat Irgil, Ilker Yesilkaya, Mohammad T. Khasawneh

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

According to the National Institute of Mental Health, for every five adults, one of them lives with a mental disorder such as depression, bipolar disorder, schizophrenia, and anxiety. Such disorders could lead to moderate to severe conditions (i.e., suicide). Psychiatrists use many tools and questionnaires to detect mental disorders and one the most common is Symptom Checklist-90-Revised (SCL-90-R) test, which composed of 90 questions and can detect symptoms for ten different mental disorders. This research proposes a new artificial intelligent framework for detecting mental disorders. The SCL-90-R questionnaire has been sent to more than 500 users in Turkey through a web system and evaluated by a psychiatrist. Then, a prediction model is developed by using Regularized Multinomial Logistic Regression (REM-LR). The results show that the accuracy of the final model is 97%. Experts and users can use the proposed model for enhancing clinical decisions.

Original language	English (US)
Title of host publication	IISE Annual Conference and Expo 2019
Publisher	Institute of Industrial and Systems Engineers, IISE
ISBN (Electronic)	9781713814092
State	Published - 2019
Event	2019 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2019 - Orlando, United States Duration: May 18 2019 → May 21 2019

Publication series

Name	IISE Annual Conference and Expo 2019

Conference

Conference	2019 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2019
Country	United States
City	Orlando
Period	5/18/19 → 5/21/19

Keywords

Logistic Regression
Machine Learning
Mental Healthcare
Regularization
SCL-90-R test

UN SDGs

This output contributes to the following Sustainable Development Goal(s)

Fingerprint Dive into the research topics of 'Detecting psychological symptom patterns using regularized multinomial logistic regression'. Together they form a unique fingerprint.

Cite this

Detecting psychological symptom patterns using regularized multinomial logistic regression. / Tutun, Salih; Ahmed, Abdulaziz A.; Irgil, Sedat; Yesilkaya, Ilker; Khasawneh, Mohammad T.

IISE Annual Conference and Expo 2019. Institute of Industrial and Systems Engineers, IISE, 2019. 967087 (IISE Annual Conference and Expo 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Tutun, S, Ahmed, AA, Irgil, S, Yesilkaya, I & Khasawneh, MT 2019, Detecting psychological symptom patterns using regularized multinomial logistic regression. in IISE Annual Conference and Expo 2019., 967087, IISE Annual Conference and Expo 2019, Institute of Industrial and Systems Engineers, IISE, 2019 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2019, Orlando, United States, 5/18/19.

@inproceedings{b700219fd77a454eb553a7a1487059de,

title = "Detecting psychological symptom patterns using regularized multinomial logistic regression",

abstract = "According to the National Institute of Mental Health, for every five adults, one of them lives with a mental disorder such as depression, bipolar disorder, schizophrenia, and anxiety. Such disorders could lead to moderate to severe conditions (i.e., suicide). Psychiatrists use many tools and questionnaires to detect mental disorders and one the most common is Symptom Checklist-90-Revised (SCL-90-R) test, which composed of 90 questions and can detect symptoms for ten different mental disorders. This research proposes a new artificial intelligent framework for detecting mental disorders. The SCL-90-R questionnaire has been sent to more than 500 users in Turkey through a web system and evaluated by a psychiatrist. Then, a prediction model is developed by using Regularized Multinomial Logistic Regression (REM-LR). The results show that the accuracy of the final model is 97%. Experts and users can use the proposed model for enhancing clinical decisions.",

keywords = "Logistic Regression, Machine Learning, Mental Healthcare, Regularization, SCL-90-R test",

author = "Salih Tutun and Ahmed, {Abdulaziz A.} and Sedat Irgil and Ilker Yesilkaya and Khasawneh, {Mohammad T.}",

note = "Publisher Copyright: {\textcopyright} 2019 IISE Annual Conference and Expo 2019. All rights reserved. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 2019 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2019 ; Conference date: 18-05-2019 Through 21-05-2019",

year = "2019",

language = "English (US)",

series = "IISE Annual Conference and Expo 2019",

publisher = "Institute of Industrial and Systems Engineers, IISE",

booktitle = "IISE Annual Conference and Expo 2019",

}

TY - GEN

T1 - Detecting psychological symptom patterns using regularized multinomial logistic regression

AU - Tutun, Salih

AU - Ahmed, Abdulaziz A.

AU - Irgil, Sedat

AU - Yesilkaya, Ilker

AU - Khasawneh, Mohammad T.

PY - 2019

Y1 - 2019

N2 - According to the National Institute of Mental Health, for every five adults, one of them lives with a mental disorder such as depression, bipolar disorder, schizophrenia, and anxiety. Such disorders could lead to moderate to severe conditions (i.e., suicide). Psychiatrists use many tools and questionnaires to detect mental disorders and one the most common is Symptom Checklist-90-Revised (SCL-90-R) test, which composed of 90 questions and can detect symptoms for ten different mental disorders. This research proposes a new artificial intelligent framework for detecting mental disorders. The SCL-90-R questionnaire has been sent to more than 500 users in Turkey through a web system and evaluated by a psychiatrist. Then, a prediction model is developed by using Regularized Multinomial Logistic Regression (REM-LR). The results show that the accuracy of the final model is 97%. Experts and users can use the proposed model for enhancing clinical decisions.

AB - According to the National Institute of Mental Health, for every five adults, one of them lives with a mental disorder such as depression, bipolar disorder, schizophrenia, and anxiety. Such disorders could lead to moderate to severe conditions (i.e., suicide). Psychiatrists use many tools and questionnaires to detect mental disorders and one the most common is Symptom Checklist-90-Revised (SCL-90-R) test, which composed of 90 questions and can detect symptoms for ten different mental disorders. This research proposes a new artificial intelligent framework for detecting mental disorders. The SCL-90-R questionnaire has been sent to more than 500 users in Turkey through a web system and evaluated by a psychiatrist. Then, a prediction model is developed by using Regularized Multinomial Logistic Regression (REM-LR). The results show that the accuracy of the final model is 97%. Experts and users can use the proposed model for enhancing clinical decisions.

KW - Logistic Regression

KW - Machine Learning

KW - Mental Healthcare

KW - Regularization

KW - SCL-90-R test

UR - http://www.scopus.com/inward/record.url?scp=85095452203&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85095452203&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85095452203

T3 - IISE Annual Conference and Expo 2019

BT - IISE Annual Conference and Expo 2019

PB - Institute of Industrial and Systems Engineers, IISE

T2 - 2019 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2019

Y2 - 18 May 2019 through 21 May 2019

ER -