Suicide is understood as a series of actions including suicidal ideation, suicidal planning, and suicide attempt [1]. Suicidal ideation refers to immersion into thoughts about suicide-related behaviors [2], and suicidal planning refers to specific planning to attempt suicide, which can lead to suicide attempt and ultimately death. If suicidal ideation is focused on the aspect of thinking, suicide attempt is the outward expression as a specific action. Suicide attempt includes suicide behaviors committed without the intention of suicide to achieve other goals as well as cases in which an individual attempted to kill oneself but failed.

According to OECD suicide statistics, Korea had the highest suicide rate among 35 OECD countries in 2017 at 23.0 per 100,000 population [3]. The 2019 suicide statistics published by Statistics Korea showed that suicide is the leading cause of death among individuals aged 10-39 years [4], and suicide has continuously been the leading cause of death among the teens between the age 10-19 since 2008.

Not all adolescents with suicidal ideation actually attempt suicide, and groups of adolescents without suicidal planning may attempt to commit suicide [5]. However, because suicidal ideation is highly likely to progress to suicide attempt and suicidal death, suicide attempts can be reduced by reducing suicidal ideation [6]. In fact, approximately one-third of adolescents with suicidal ideation are known to make a suicide attempt [5], and preventive interventions are crucial because suicidal ideation is an important predictor of suicide attempt [7,8]. Hence, there are growing interests on suicide risk screening in clinical settings, and classifying high-risk groups of suicide with suicidal ideation is crucial for a more effective suicide preventive intervention [9].

There are several factors that influence adolescents’ suicide risk [10], and specifically personality trait is one such risk factor [11]. Some past studies have analyzed features of groups with a suicide risk using the Personality Assessment Inventory (PAI) [12,13], but these studies were limited to adult subjects, with little relevant research on children and adolescents according to our literature review. In addition, most studies simply examined the association of suicide risk with symptoms such as depression and anxiety or identified relevant risk factors [14]. Recently, some studies have applied machine learning in the prediction of suicide [15], and there was an attempt to classify or predict adolescents with a suicide risk using machine learning [15-17]. However, none of the studies attempted to predict adolescents with high suicide risk using machine learning in consideration of their personality traits. Thus, this study aims to classify groups at risk of suicide by including personality traits using the PAI.

Previous statistical techniques were limited because they tried to predict suicide risk, a complex problem, using a simple algorithm [18]. Machine learning differs from the traditional statistical techniques in that it generates the most optimal algorithm from various predictors, based on which it has been proposed to be useful in the prediction and classification of suicide risk [19]. In the present study, we aim to analyze the PAI profiles of child and adolescent patients who received outpatient psychiatric care using machine learning techniques, such as logistic regression (LR), random forest (RF), artificial neural network (ANN), support vector machine (SVM), and extreme gradient boosting (XGB), to develop and validate a classification model for individuals with high suicide risk.

This study attempted to develop and validate a model for predicting high suicide risk in child and adolescent psychiatric patients using the scales of PAI-A and applying machine learning techniques.

In this study, approximately 35.5% (n=44) of the participants were classified as the high suicide risk group, as defined by suicide ideation scale score of 60 or higher. Jeon et al. [24] reported the lifetime prevalence of suicidal ideation to be 18.9% among adolescents aged 12-18 years, and the higher prevalence in our study may be attributable to the fact that the study population consisted of patients who sought psychiatric care, who have various risk factors and psychopathologies, as opposed to the general population.

According to a study comparing the psychometric variables of high and low suicide risk groups by Heo et al. [25], the high suicidal ideation group had significantly higher scores on the clinical scales of PAI, namely SOM, ANX, ARD, DEP, MAN, PAR, BOR, ANT, and DRG scales, compared to the low suicidal ideation group. In the study of suicide attempt by Sinclair et al. [12], total nine indicators (ICN, NIM, STR, MAN-G, SCZ-T, BOR-N, BOR-S, ANT-A, and ANT-E) among 42 PAI indicators and specifically six indicators (NIM, STR, MAN-G, BOR-N, BOR-S, ANT-A) when considering several factors such as age, sex, education, and race were found to differ between multiple suicide attempt and single/no suicide attempt. The scales found to be significantly associated with suicide risk classification in our study, namely ARD, NON, DEP, ANX, RXR, AGG, and STR scales, differed from those reported by previous studies, and this is presumed to be due to the adolescent study population in our study and application of machine learning methods.

AGG was found to be a significant factor in classifying high suicide risk group. It has been suggested as a developmental factor of suicide in a previous study [26], and it has also been reported to be associated with ADHD, ODD, and conduct disorders among children and adolescents, with adolescents with a suicide attempt history showed higher impulsivity than those without a suicide attempt history [27]. In the developmental aspect of neuromaturation, adolescent’s aggression can be interpreted in the context that structural and functional maturation of the prefrontal cortex is in progress, and it is still difficult to demonstrate consistent impulse control [28,29]. Adolescents with conduct disorder showed a high suicide risk, and some studies have linked this to severe emotional dysregulation and higher prevalence of mood disorder [30]. Substance abuse is also a risk factor of suicide attempt in adolescents, and one study reported that heavy drinking increases psychological distress and aggressiveness and hinders adaptive coping strategy, thereby increasing suicide risk [31].

NON scale indicates a lack of social support, and this scale can be used to examine the degree of support provided by family members and friends. A previous study reported that female adolescents who are socially severed from their friends engage in more suicidal ideation, while male adolescents who belong in a tightly networked school community exhibit a protective effect against suicide attempt [32]. Furthermore, this is in line with the findings that family stress or familial conflict directly and indirectly influence adolescents’ suicide risk behaviors [33-35].

Anxiety disorder and PTSD are known to increase suicide risk [26]. In addition, patients with depression are 13-26 times higher risk of suicide compared to the general population [36], and a psychological autopsy study on 229 suicidal deaths found that 59% of these people had major depressive disorder [37]. In our study, anxiety as well as depression were identified as risk factors for suicide, in this regard previous study [38] shown that the coexistence of depressive and anxiety symptoms was associated with an increased risk of suicide compared to having either one.

RXR evaluate the interest in psychological and emotional change, indicating that individual with high scores lack therapeutic motivation. According to a study of untreated adolescent depression [39], those who were not treated were found to have 4.19 times higher risk of suicide than those who were treated or who were the first depressive episode.

This study has several strengths. First, the study population comprised child and adolescent psychiatric patients. Second, data were analyzed using machine learning techniques. Suicide is a complex psychological phenomenon that occurs as a result of numerous factors. A past study confirmed the importance of machine learning techniques compared to other existing methodologies that utilize data in testing a priori hypothesis when testing the interactions among various complex factors [19], and the predictive accuracy was higher with machine learning techniques. In this study, we developed and validated a classification model for high suicide risk group using various psychopathologies, including personality traits with a standardized instrument. There were no studies that analyzed the risk factors of suicide in adolescents by both assessing them under a single standardized tool that can evaluate comorbid psychopathologies and applying machine learning techniques. The Boruta algorithm has an advantage of eliminating researcher’s intention in feature selection, and we were able to confirm that the predictive model has better performance in such case. Third, in light of past studies on the association between suicide risk and intelligence, we set an intelligence score of 70 or higher as an inclusion criterion and included intelligence in the high suicide risk classification model for analysis. A previous study reported an inverse association between IQ and suicide risk in children and adolescents, and it has been found that those with a low IQ who have no appropriate coping and cognitive skills are vulnerable to in suicidal ideation upon a stressful life event, as a result suicidal ideation and planning ultimately leads to a suicide attempt [40,41].

There are some limitations to this study. The first one is limited generalizability since subjects in this study has been selected among patient group who have visited child and adolescent psychiatric clinic in university hospital. Second, we used a self-report test. A retrospective self-report test is vulnerable to recall bias or underreporting. Subsequent studies should supplement the self-reported data with structured interviews. Third, causation cannot be drawn due to the cross-sectional design. However, this study has clinical significance in that various facets of personality and psychopathology related to suicide risk were included in the analysis.

In conclusion, this study developed a model to predict high suicide risk groups by applying machine learning techniques to PAI-A data. The model had an AUC of 0.936, confirming its potential for classifying and predicting high suicide risk groups with a “excellent” diagnostic accuracy [42]. In comparison to previous study findings using PAI, AGG, NON, and RXR were added as an important consideration in this study, and it may be helpful to detect suicides risk if these scales are actually elevated in clinical settings. However, suicide must be assessed based on multiple aspects, and although PAI-A assess an array of domains, further research is needed for predicting high suicide risk groups. In addition, large-scale data obtained from multiple institutions and further research will be needed to enhance the performance of the developed model and improves its applicability in clinical settings. Such model would be used for assessing and screening patients with high suicide risk in clinical practice.