Journal
SCIENTIFIC REPORTS
Volume 11, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41598-021-94839-5
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Funding
- Basic Science Research Program of the National Research Foundation of Korea (NRF) - Ministry of Education [NRF-2018R1D1A1B07050245]
- Technology Innovation Program [20012931]
- Ministry of Trade, Industry and Energy (MOTIE, Korea)
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This study evaluated the utility of MMPI-2 in assessing suicidal risk using random forest and KNN techniques. Results showed that machine learning using MMPI-2 provided reliable accuracy in classifying and predicting suicidal ideation and past suicidal attempts, with high accuracy rates using both methods.
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.
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