PMID- 34071385
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20210627
IS  - 2075-4418 (Print)
IS  - 2075-4418 (Electronic)
IS  - 2075-4418 (Linking)
VI  - 11
IP  - 6
DP  - 2021 May 28
TI  - Can the MMPI Predict Adult ADHD? An Approach Using Machine Learning Methods.
LID - 10.3390/diagnostics11060976 [doi]
LID - 976
AB  - (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.
FAU - Kim, Sunhae
AU  - Kim S
AUID- ORCID: 0000-0001-7443-7684
AD  - Department of Psychiatry, Hanyang University Medical Center, Seoul 04763, Korea.
FAU - Lee, Hye-Kyung
AU  - Lee HK
AD  - Department of Nursing, College of Nursing and Health, Kongju National University, 
      Gongju 32588, Korea.
FAU - Lee, Kounseok
AU  - Lee K
AUID- ORCID: 0000-0002-6084-5043
AD  - Department of Psychiatry, Hanyang University Medical Center, Seoul 04763, Korea.
LA  - eng
GR  - NRF-2018R1D1A1B07050245/National Research Foundation of Korea/
GR  - 20012931/Ministry of Trade, Industry and Energy (MOTIE, Korea)./
PT  - Journal Article
DEP - 20210528
PL  - Switzerland
TA  - Diagnostics (Basel)
JT  - Diagnostics (Basel, Switzerland)
JID - 101658402
PMC - PMC8229212
OTO - NOTNLM
OT  - MMPI-2
OT  - adult ADHD
OT  - detection
OT  - machine learning
OT  - screening
COIS- The authors declare no conflict of interest.
EDAT- 2021/06/03 06:00
MHDA- 2021/06/03 06:01
PMCR- 2021/05/28
CRDT- 2021/06/02 01:31
PHST- 2021/03/15 00:00 [received]
PHST- 2021/04/25 00:00 [revised]
PHST- 2021/05/27 00:00 [accepted]
PHST- 2021/06/02 01:31 [entrez]
PHST- 2021/06/03 06:00 [pubmed]
PHST- 2021/06/03 06:01 [medline]
PHST- 2021/05/28 00:00 [pmc-release]
AID - diagnostics11060976 [pii]
AID - diagnostics-11-00976 [pii]
AID - 10.3390/diagnostics11060976 [doi]
PST - epublish
SO  - Diagnostics (Basel). 2021 May 28;11(6):976. doi: 10.3390/diagnostics11060976.