Detecting suicidal risk using MMPI-2 based on machine learning algorithm

Abstract Minnesota Multiphasic Personality Inventory-2 (MMPI-2) is a widely used tool for early detection of psychological maladjustment and assessing the level of adaptation for a large group in clinical settings, schools, and corporations. This study aims to evaluate the utility of MMPI-2 in asses...

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Autores principales:	Sunhae Kim, Hye-Kyung Lee, Kounseok Lee
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/595e11dfed634aa4b873d35c0146527a
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spelling	oai:doaj.org-article:595e11dfed634aa4b873d35c0146527a2021-12-02T16:31:48ZDetecting suicidal risk using MMPI-2 based on machine learning algorithm10.1038/s41598-021-94839-52045-2322https://doaj.org/article/595e11dfed634aa4b873d35c0146527a2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94839-5https://doaj.org/toc/2045-2322Abstract Minnesota Multiphasic Personality Inventory-2 (MMPI-2) is a widely used tool for early detection of psychological maladjustment and assessing the level of adaptation for a large group in clinical settings, schools, and corporations. This study aims to evaluate the utility of MMPI-2 in assessing suicidal risk using the results of MMPI-2 and suicidal risk evaluation. A total of 7,824 datasets collected from college students were analyzed. The MMPI-2-Resturcutred Clinical Scales (MMPI-2-RF) and the response results for each question of the Mini International Neuropsychiatric Interview (MINI) suicidality module were used. For statistical analysis, random forest and K-Nearest Neighbors (KNN) techniques were used with suicidal ideation and suicide attempt as dependent variables and 50 MMPI-2 scale scores as predictors. On applying the random forest method to suicidal ideation and suicidal attempts, the accuracy was 92.9% and 95%, respectively, and the Area Under the Curves (AUCs) were 0.844 and 0.851, respectively. When the KNN method was applied, the accuracy was 91.6% and 94.7%, respectively, and the AUCs were 0.722 and 0.639, respectively. The study confirmed that machine learning using MMPI-2 for a large group provides reliable accuracy in classifying and predicting the subject's suicidal ideation and past suicidal attempts.Sunhae KimHye-Kyung LeeKounseok LeeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sunhae Kim Hye-Kyung Lee Kounseok Lee Detecting suicidal risk using MMPI-2 based on machine learning algorithm
description	Abstract Minnesota Multiphasic Personality Inventory-2 (MMPI-2) is a widely used tool for early detection of psychological maladjustment and assessing the level of adaptation for a large group in clinical settings, schools, and corporations. This study aims to evaluate the utility of MMPI-2 in assessing suicidal risk using the results of MMPI-2 and suicidal risk evaluation. A total of 7,824 datasets collected from college students were analyzed. The MMPI-2-Resturcutred Clinical Scales (MMPI-2-RF) and the response results for each question of the Mini International Neuropsychiatric Interview (MINI) suicidality module were used. For statistical analysis, random forest and K-Nearest Neighbors (KNN) techniques were used with suicidal ideation and suicide attempt as dependent variables and 50 MMPI-2 scale scores as predictors. On applying the random forest method to suicidal ideation and suicidal attempts, the accuracy was 92.9% and 95%, respectively, and the Area Under the Curves (AUCs) were 0.844 and 0.851, respectively. When the KNN method was applied, the accuracy was 91.6% and 94.7%, respectively, and the AUCs were 0.722 and 0.639, respectively. The study confirmed that machine learning using MMPI-2 for a large group provides reliable accuracy in classifying and predicting the subject's suicidal ideation and past suicidal attempts.
format	article
author	Sunhae Kim Hye-Kyung Lee Kounseok Lee
author_facet	Sunhae Kim Hye-Kyung Lee Kounseok Lee
author_sort	Sunhae Kim
title	Detecting suicidal risk using MMPI-2 based on machine learning algorithm
title_short	Detecting suicidal risk using MMPI-2 based on machine learning algorithm
title_full	Detecting suicidal risk using MMPI-2 based on machine learning algorithm
title_fullStr	Detecting suicidal risk using MMPI-2 based on machine learning algorithm
title_full_unstemmed	Detecting suicidal risk using MMPI-2 based on machine learning algorithm
title_sort	detecting suicidal risk using mmpi-2 based on machine learning algorithm
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/595e11dfed634aa4b873d35c0146527a
work_keys_str_mv	AT sunhaekim detectingsuicidalriskusingmmpi2basedonmachinelearningalgorithm AT hyekyunglee detectingsuicidalriskusingmmpi2basedonmachinelearningalgorithm AT kounseoklee detectingsuicidalriskusingmmpi2basedonmachinelearningalgorithm
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Detecting suicidal risk using MMPI-2 based on machine learning algorithm

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