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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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. |
format | Online Article Text |
id | pubmed-8229212 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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