Abstract
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.
Highlights
Machine learning (ML) is defined as a computational strategy that automatically determines methods and parameters to arrive at an optimal solution to a problem, rather than preprogramming by humans to present a fixed s olution[1]
Among the 7824 participants, 3685 (47.1%) were male, a total of 673 (8.6%) participants classified as a suicidal ideation group, and 404 (5.4%) were classified as a suicidal attempt group (Table 1)
Prediction accuracy of the random forest method was 92.9% for suicidal ideation and 95% for suicidal attempts; k-Nearest Neighbors (KNN) accurately predicted 91.6% of suicidal ideation and 94.7% of suicidal attempts (Table 2)
Summary
Machine learning (ML) is defined as a computational strategy that automatically determines methods and parameters to arrive at an optimal solution to a problem, rather than preprogramming by humans to present a fixed s olution[1]. Machine learning is the study and application of algorithms and systems that can improve knowledge or performance through experience. Machine learning algorithms can be changed and improved when exposed to new data, so these detection patterns have the advantages of efficiency, complexity, and flexibility[3]. Attempt and completion are closely related to impulsivity and aggression[16] They share many neurobiological correlates[14,17,18,19], comorbidity of psychiatric disorders such as mood disorder, borderline personality disorder, and substance use disorder (SUD)[20,21,22,23,24,25,26,27,28]. It will be important to screen various risk factors and psychopathology to determine suicide risk
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