Can the MMPI Predict Adult ADHD? An Approach Using Machine Learning Methods
Abstract
:1. Introduction
2. Materials and Methods
3. Results
3.1. Experimental Results
3.1.1. General Characteristics
3.1.2. Prediction Accuracy of ML Model
3.1.3. AUC of ML Model
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Dimension | Scale | ASRS (−) | ASRS (+) | t | p | Cohen’s d | ||||
---|---|---|---|---|---|---|---|---|---|---|
Validity Indicators | VRIN_r | 4.19 | ± | 2.37 | 5.27 | ± | 2.69 | −8.51 | <0.001 | −0.45 |
TRIN_r | 10.37 | ± | 1.97 | 11.25 | ± | 2.27 | −8.28 | <0.001 | −0.44 | |
F_r | 3.57 | ± | 3.35 | 7.59 | ± | 4.64 | −21.99 | <0.001 | −1.17 | |
Fp_r | 2.18 | ± | 2.02 | 3.86 | ± | 2.88 | −15.12 | <0.001 | −0.80 | |
Fs | 2.15 | ± | 1.94 | 4.14 | ± | 2.47 | −18.90 | <0.001 | −1.00 | |
FBS_r | 9.11 | ± | 3.48 | 12.01 | ± | 4.07 | −15.50 | <0.001 | −0.82 | |
L_r | 4.46 | ± | 2.24 | 3.25 | ± | 2.09 | 10.24 | <0.001 | 0.54 | |
K_r | 7.24 | ± | 2.67 | 4.77 | ± | 2.29 | 17.54 | <0.001 | 0.93 | |
Higher- Order (H-O) | EID | 13.99 | ± | 6.60 | 20.87 | ± | 7.22 | −19.55 | <0.001 | −1.04 |
THD | 2.55 | ± | 2.70 | 4.86 | ± | 3.95 | −15.61 | <0.001 | −0.83 | |
BXD | 6.16 | ± | 2.96 | 8.47 | ± | 3.04 | −14.68 | <0.001 | −0.78 | |
Restructured Clinical (RC) | RCd | 7.38 | ± | 5.17 | 13.54 | ± | 5.40 | −22.41 | <0.001 | −1.19 |
RC1 | 6.62 | ± | 3.93 | 10.17 | ± | 4.83 | −16.75 | <0.001 | −0.89 | |
RC2 | 5.53 | ± | 3.11 | 6.57 | ± | 3.20 | −6.32 | <0.001 | −0.34 | |
RC3 | 4.76 | ± | 2.58 | 6.69 | ± | 2.81 | −13.99 | <0.001 | −0.74 | |
RC4 | 4.03 | ± | 2.60 | 6.21 | ± | 3.03 | −15.60 | <0.001 | −0.83 | |
RC6 | 1.46 | ± | 1.83 | 2.99 | ± | 2.78 | −15.07 | <0.001 | −0.80 | |
RC7 | 7.78 | ± | 4.31 | 12.59 | ± | 4.47 | −20.98 | <0.001 | −1.11 | |
RC8 | 3.18 | ± | 2.59 | 5.72 | ± | 3.22 | −18.17 | <0.001 | −0.96 | |
RC9 | 11.28 | ± | 4.61 | 14.71 | ± | 4.01 | −14.19 | <0.001 | −0.75 | |
Content, Clinical Subscale | MLS | 3.02 | ± | 1.60 | 3.97 | ± | 1.77 | −11.08 | <0.001 | −0.59 |
GIC | 0.64 | ± | 1.04 | 1.21 | ± | 1.36 | −9.99 | <0.001 | −0.53 | |
HPC | 1.31 | ± | 1.37 | 2.25 | ± | 1.74 | −12.71 | <0.001 | −0.67 | |
NUC | 2.99 | ± | 1.69 | 4.02 | ± | 1.74 | −11.56 | <0.001 | −0.61 | |
COG | 3.06 | ± | 2.13 | 5.68 | ± | 2.16 | −23.18 | <0.001 | −1.23 | |
SUI | 0.29 | ± | 0.74 | 0.82 | ± | 1.25 | −12.72 | <0.001 | −0.67 | |
HLP | 1.00 | ± | 1.07 | 1.82 | ± | 1.28 | −14.23 | <0.001 | −0.76 | |
SFD | 1.41 | ± | 1.26 | 2.45 | ± | 1.28 | −15.54 | <0.001 | −0.82 | |
NFC | 4.01 | ± | 2.16 | 5.73 | ± | 1.98 | −15.13 | <0.001 | −0.80 | |
STW | 2.76 | ± | 1.72 | 4.20 | ± | 1.76 | −15.77 | <0.001 | −0.84 | |
AXY | 0.42 | ± | 0.82 | 1.16 | ± | 1.25 | −16.50 | <0.001 | −0.88 | |
ANP | 2.37 | ± | 1.64 | 3.78 | ± | 1.75 | −16.07 | <0.001 | −0.85 | |
BRF | 2.00 | ± | 1.55 | 2.73 | ± | 1.77 | −8.75 | <0.001 | −0.46 | |
MSF | 3.97 | ± | 2.31 | 4.03 | ± | 2.32 | −0.45 | 0.66 | −0.02 | |
JCP | 0.83 | ± | 1.07 | 1.48 | ± | 1.39 | −11.35 | <0.001 | −0.60 | |
SUB | 0.70 | ± | 0.95 | 1.16 | ± | 1.32 | −8.79 | <0.001 | −0.47 | |
AGG | 2.51 | ± | 1.85 | 3.90 | ± | 1.99 | −14.19 | <0.001 | −0.75 | |
ACT | 2.36 | ± | 1.74 | 3.61 | ± | 1.73 | −13.57 | <0.001 | −0.72 | |
FML | 2.07 | ± | 1.85 | 3.73 | ± | 2.22 | −16.72 | <0.001 | −0.89 | |
IPP | 4.37 | ± | 2.18 | 4.50 | ± | 2.16 | −1.12 | 0.26 | −0.06 | |
SAV | 4.16 | ± | 2.58 | 4.54 | ± | 2.67 | −2.73 | 0.01 | −0.15 | |
SHY | 3.33 | ± | 2.01 | 4.27 | ± | 1.92 | −8.91 | <0.001 | −0.47 | |
DSF | 0.78 | ± | 1.12 | 1.37 | ± | 1.46 | −9.61 | <0.001 | −0.51 | |
AES | 2.94 | ± | 1.69 | 3.12 | ± | 1.76 | −2.04 | 0.04 | −0.11 | |
MEC | 2.22 | ± | 1.81 | 2.41 | ± | 1.88 | −1.95 | 0.05 | −0.10 | |
Personality Psychopathology Five (PSY-5) | AGGR_r | 7.85 | ± | 3.23 | 8.56 | ± | 3.14 | −4.16 | <0.001 | −0.22 |
PSYC_r | 3.07 | ± | 2.79 | 5.60 | ± | 3.89 | −16.58 | <0.001 | −0.88 | |
DISC_r | 5.73 | ± | 2.54 | 7.16 | ± | 2.67 | −10.61 | <0.001 | −0.56 | |
NEGE_r | 7.88 | ± | 3.87 | 11.43 | ± | 3.72 | −17.36 | <0.001 | −0.92 | |
INTR_r | 8.06 | ± | 3.82 | 8.26 | ± | 3.74 | −1.01 | 0.31 | −0.05 |
Trees or Nearest Neighbors | Validation Accuracy | Test Accuracy | OOB Accuracy | |
---|---|---|---|---|
K-Nearest Neighbors Classification | 7 | 0.927 | 0.931 | |
Linear Discriminant Analysis | 0.912 | |||
Random Forest Classification | 40 | 0.944 | 0.936 | 0.078 |
Precision | Recall | F1 Score | AUC | |
---|---|---|---|---|
K-Nearest Neighbors Classification | 0.900 | 0.931 | 0.909 | 0.722 |
Linear Discriminant Analysis | 0.899 | 0.912 | 0.905 | 0.806 |
Random Forest Classification | 0.916 | 0.936 | 0.909 | 0.790 |
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Kim, S.; Lee, H.-K.; Lee, K. Can the MMPI Predict Adult ADHD? An Approach Using Machine Learning Methods. Diagnostics 2021, 11, 976. https://doi.org/10.3390/diagnostics11060976
Kim S, Lee H-K, Lee K. Can the MMPI Predict Adult ADHD? An Approach Using Machine Learning Methods. Diagnostics. 2021; 11(6):976. https://doi.org/10.3390/diagnostics11060976
Chicago/Turabian StyleKim, Sunhae, Hye-Kyung Lee, and Kounseok Lee. 2021. "Can the MMPI Predict Adult ADHD? An Approach Using Machine Learning Methods" Diagnostics 11, no. 6: 976. https://doi.org/10.3390/diagnostics11060976