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Can the MMPI Predict Adult ADHD? An Approach Using Machine Learning Methods

(1) Background: Adult attention-deficit/hyperactivity disorder (ADHD) symptoms cause various social difficulties due to attention deficit and impulsivity. In addition, in contrast to ADHD in childhood, ADHD in adulthood is difficult to diagnose due to mixed psychopathologies. This study aimed to det...

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Autores principales: Kim, Sunhae, Lee, Hye-Kyung, Lee, Kounseok
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8229212/
https://www.ncbi.nlm.nih.gov/pubmed/34071385
http://dx.doi.org/10.3390/diagnostics11060976
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author Kim, Sunhae
Lee, Hye-Kyung
Lee, Kounseok
author_facet Kim, Sunhae
Lee, Hye-Kyung
Lee, Kounseok
author_sort Kim, Sunhae
collection PubMed
description (1) Background: Adult attention-deficit/hyperactivity disorder (ADHD) symptoms cause various social difficulties due to attention deficit and impulsivity. In addition, in contrast to ADHD in childhood, ADHD in adulthood is difficult to diagnose due to mixed psychopathologies. This study aimed to determine whether it is possible to predict ADHD symptoms in adults using the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) with machine learning (ML) techniques; (2) Methods: Data collected from 5726 college students were analyzed. The MMPI-2-Restructured Form (MMPI-2-RF) was used, and ADHD symptoms in adults were evaluated using the Attention-Deficit/Hyperactivity Disorder Self-Report Scale (ASRS). For statistical analysis, three ML algorithms were used, i.e., K-nearest neighbors (KNN), linear discriminant analysis (LDA), and random forest, with the ASRS evaluation result as the dependent variable and the 50 MMPI-2-RF scales as predictors; (3) Results: When the KNN, LDA, and random forest techniques were applied, the accuracy was 93.1%, 91.2%, and 93.6%, respectively, and the area under the curve (AUC) was 0.722, 0.806, and 0.790, respectively. The AUC of the LDA method was the largest, with an excellent level of diagnostic accuracy; (4) Conclusions: ML using the MMPI-2 in a large group could provide reliable accuracy in screening for adult ADHD.
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spelling pubmed-82292122021-06-26 Can the MMPI Predict Adult ADHD? An Approach Using Machine Learning Methods Kim, Sunhae Lee, Hye-Kyung Lee, Kounseok Diagnostics (Basel) Article (1) Background: Adult attention-deficit/hyperactivity disorder (ADHD) symptoms cause various social difficulties due to attention deficit and impulsivity. In addition, in contrast to ADHD in childhood, ADHD in adulthood is difficult to diagnose due to mixed psychopathologies. This study aimed to determine whether it is possible to predict ADHD symptoms in adults using the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) with machine learning (ML) techniques; (2) Methods: Data collected from 5726 college students were analyzed. The MMPI-2-Restructured Form (MMPI-2-RF) was used, and ADHD symptoms in adults were evaluated using the Attention-Deficit/Hyperactivity Disorder Self-Report Scale (ASRS). For statistical analysis, three ML algorithms were used, i.e., K-nearest neighbors (KNN), linear discriminant analysis (LDA), and random forest, with the ASRS evaluation result as the dependent variable and the 50 MMPI-2-RF scales as predictors; (3) Results: When the KNN, LDA, and random forest techniques were applied, the accuracy was 93.1%, 91.2%, and 93.6%, respectively, and the area under the curve (AUC) was 0.722, 0.806, and 0.790, respectively. The AUC of the LDA method was the largest, with an excellent level of diagnostic accuracy; (4) Conclusions: ML using the MMPI-2 in a large group could provide reliable accuracy in screening for adult ADHD. MDPI 2021-05-28 /pmc/articles/PMC8229212/ /pubmed/34071385 http://dx.doi.org/10.3390/diagnostics11060976 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kim, Sunhae
Lee, Hye-Kyung
Lee, Kounseok
Can the MMPI Predict Adult ADHD? An Approach Using Machine Learning Methods
title Can the MMPI Predict Adult ADHD? An Approach Using Machine Learning Methods
title_full Can the MMPI Predict Adult ADHD? An Approach Using Machine Learning Methods
title_fullStr Can the MMPI Predict Adult ADHD? An Approach Using Machine Learning Methods
title_full_unstemmed Can the MMPI Predict Adult ADHD? An Approach Using Machine Learning Methods
title_short Can the MMPI Predict Adult ADHD? An Approach Using Machine Learning Methods
title_sort can the mmpi predict adult adhd? an approach using machine learning methods
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8229212/
https://www.ncbi.nlm.nih.gov/pubmed/34071385
http://dx.doi.org/10.3390/diagnostics11060976
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