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Regarding prediction, machine learning technology has advantages in accuracy and scalability compared to conventional statistical approaches 3. Several studies have been conducted on MMPI-2 in particular as it is useful and expandable 8. For example, some studies use machine learning, the Structured Inventory of Malingered Symptomatology (SIMS) scale 6, and the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) scale to discriminate malingering to obtain external benefits 7. Some studies apply machine learning to differentiate between various types of psychopathology. It can be applied to improve the effectiveness and goals of prevention programs and interventions 1. Machine learning is specifically useful in predicting human behavior, including high-risk behavior.
In recent studies, machine learning is applied to big data in medical and health fields for disease diagnosis, treatment, and prevention 5. In psychiatry, machine learning applications have been proposed to improve diagnostic and prognostic accuracy and determine treatment options 4. Furthermore, 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. By making the data understandable from the start, machines can detect complex and meaningful data patterns, which may be difficult or impossible for humans to derive.
The basic premise of machine learning is the assumption that a machine can learn from data, recognize patterns in data, and understand data with minimal human intervention. Machine learning is the study and application of algorithms and systems that can improve knowledge or performance through experience. Moreover, machine learning algorithms are integrated into everyday life as internet searches and product recommendations, translation services, speech recognition services, and autonomous vehicles 2. 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 solution 1. 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.
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. 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. 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. 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. A total of 7,824 datasets collected from college students were analyzed. This study aims to evaluate the utility of MMPI-2 in assessing suicidal risk using the results of MMPI-2 and suicidal risk evaluation. 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.
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