![]() 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. Indicators of MMPI-2-RF acceptance can be cited, and criticisms of the MMPI-2-RF can be addressed with information available in the test documents and an extensive, modern, and actively growing peer-reviewed literature.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. Information about the known and potential rate of error associated with MMPI-2-RF scores is available, and standard procedures for administration, scoring, and interpretation of the inventory are detailed in the test administration manual. Consideration of the MMPI-2-RF in light of the Daubert factors indicates that the instrument has been subjected to extensive empirical testing and that a substantial peer-reviewed literature is available to guide and support its use. The answers to these questions apply more broadly to testimony in depositions, pre-trial hearings, and at trial. The questions guiding this discussion are based on the Daubert factors, established in 1993 by the US Supreme Court as criteria for gauging the scientific validity of proffered expert testimony. Potential challenges to MMPI-2-RF-based testimony are identified in this article and discussed in question and answer format. In the case of the Minnesota Multiphasic Personality Inventory-2 Restructured Form (MMPI-2-RF), these challenges can be addressed by becoming familiar with the rationale for and the methods used in revising the inventory, the information contained in the test manuals, and the growing peer-reviewed literature on the test. Introduction of a new version of a psychological test brings with it challenges that can be accentuated by the adversarial nature of the legal process.
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