Detecting suicidal risk using MMPI-2 based on machine learning algorithm

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.

A Machine Learning Approach for Predicting Wage Workers’ Suicidal Ideation

“…The prediction of suicide using machine learning is mainly based on the results of suicide-related questionnaires or emotional evaluations [ 16 , 38 ]. However, the acquisition of such evaluation data may be difficult in practice, as disadvantages in work may be expected if it is targeted to actual workers.…”

“…This study used data from the Korean National Health and Nutrition Examination Survey (KHANES). Data from a total of 9 years (2007–2013, 2015, and 2017) were used [ 16 ]. In 2014, 2016, and 2018, suicidal ideation among adults was not investigated, and part-time work (full-time or part-time) was not investigated in 2019; therefore, these types of data were excluded from the study.…”

“…Among machine learning algorithms, random forest (RF) is a method for finding optimal results through randomly combining results and constructing several decision trees. Random forest is widely used due to its good predictive performance, especially in mental health-related studies [ 16 , 22 ]. Therefore, in this study, a RF was constructed to evaluate various variables, work-related variables, and the suitability of the suicidal ideation prediction model for wage workers.…”

“…Although the early identification and treatment of high-risk patients is a key suicide prevention strategy [ 14 ], high-risk patients are often not recognized by healthcare providers [ 15 ]. To overcome this issue, a suicide risk screening study using machine learning has been conducted [ 16 , 17 , 18 ]. Machine learning algorithms can change and improve as they are exposed to new data, and these detection patterns have many potential advantages over passively guided approaches to model specifications, particularly in terms of efficiency, complexity, and flexibility [ 19 ].…”

Machine Learning Approaches for Predicting Suicidal Behaviors among University Students in Bangladesh during the COVID-19 Pandemic

“…Healthcare professionals frequently fail to identify as well as diagnose high-risk patients. Numerous machine learning approaches have been suggested in psychiatry to increase diagnostic and prognosis accuracy to address this issue [15][16][17][18]. In particular, machine learning helps detect people with perilous behaviors.…”