• Psychiatry investigation
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• v.15 no.11
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• pp.1030-1036
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• 2018

Objective In this study, we aimed to develop a model predicting individuals with suicide ideation within a general population using a machine learning algorithm. Methods Among 35,116 individuals aged over 19 years from the Korea National Health & Nutrition Examination Survey, we selected 11,628 individuals via random down-sampling. This included 5,814 suicide ideators and the same number of non-suicide ideators. We randomly assigned the subjects to a training set (n=10,466) and a test set (n=1,162). In the training set, a random forest model was trained with 15 features selected with recursive feature elimination via 10-fold cross validation. Subsequently, the fitted model was used to predict suicide ideators in the test set and among the total of 35,116 subjects. All analyses were conducted in R. Results The prediction model achieved a good performance [area under receiver operating characteristic curve (AUC)=0.85] in the test set and predicted suicide ideators among the total samples with an accuracy of 0.821, sensitivity of 0.836, and specificity of 0.807. Conclusion This study shows the possibility that a machine learning approach can enable screening for suicide risk in the general population. Further work is warranted to increase the accuracy of prediction.

• Korean Journal of Biological Psychiatry
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• v.12 no.1
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• pp.3-12
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• 2005

Most suicides(about 90%) occur in the context of psychiatric disorders. Prediction of suicide risk in patients with mental illness is very important in preventing suicide attempts. However, current approaches to predict suicidality are based on clinical history and have low specificity and biological markers are not yet included. Many studies have explored the association between different biological parameters and suicidality. Studies of cerebro-spinal fluid(CSF) demonstrated that 5-HIAA and HVA levels were lower in patients with a history of suicide. Platelet serotonin transporter and the 5-HT2 serotonin receptor have also been studied in relation to violence and suicide. Depressive patients with greater suicidal tendency had significantly lower cholesterol concentrations but some researchers failed to find the correlation. DST non-supression is reported to predict suicidality in major depression. Several studies demonstrated a relationship between intron 7 polymorphism of tryptophan hydroxylase and suicidal behavior. Since suicide is not occurred in a single disease, the systematic and comprehensive study in large samples with various diagnoses is necessary to find the biological and genetic predictors of suicidal behavior.

• Korean Journal of Health Education and Promotion
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• v.30 no.3
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• pp.59-73
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• 2013

Objectives: The study is aimed at examining the individual reasons and regional/environmental factors of online search on suicide using social big data to predict practical behaviors related to suicide and to develop an online suicide prevention system on the governmental level. Methods: The study was conducted using suicide-related social big data collected from online news sites, blogs, caf$\acute{e}$s, social network services and message boards between January 1 and December 31, 2011 (321,506 buzzes from users assumed as adults and 67,742 buzzes from those assumed as teenagers). Technical analysis and development of the suicide search prediction model were done using SPSS 20.0, and the structural model, nd multi-group analysis was made using AMOS 20.0. Also, HLM 7.0 was applied for the multilevel model analysis of the determinants of search on suicide by teenagers. Results: A summary of the results of multivariate analysis is as follows. First, search on suicide by adults appeared to increase on days when there were higher number of suicide incidents, higher number of search on drinking, higher divorce rate, lower birth rate and higher average humidity. Second, search on suicide by teenagers rose on days when there were higher number of teenage suicide incidents, higher number of search on stress or drinking and less fine dust particles. Third, the comparison of the results of the structural equation model analysis of search on suicide by adults and teenagers showed that teenagers were more likely to proceed from search on stress to search on sports, drinking and suicide, while adults significantly tended to move from search on drinking to search on suicide. Fourth, the result of the multilevel model analysis of determinants of search on suicide by teenagers showed that monthly teenagers suicide rate and average humidity had positive effect on the amount of search on suicide. Conclusions: The study shows that both adults and teenagers are influenced by various reasons to experience stress and search on suicide on the Internet. Therefore, we need to develop diverse school-level programs that can help relieve teenagers of stress and workplace-level programs to get rid of the work-related stress of adults.

• Korean Journal of Culture and Social Issue
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• v.30 no.3
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• pp.239-265
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• 2024

The purpose of this study was to compare the predictive accuracy of traditional prediction models (methods) and machine learning algorithms in predicting suicidal behaviors. The research aimed to go beyond a systematic review level and scientifically examine the predictive capabilities of these two techniques through meta-analysis, analyzing variables identified through domestic research, particularly at the regional level. In order to achieve this, a total of 124 studies, including 50 studies utilizing machine learning and 74 studies employing traditional methods, were included in the meta-analysis. The results of the study revealed that the integrated area under the curve (AUC) for studies using traditional methods was .770, which was lower than the integrated AUC value of .853 for studies using machine learning. Particularly, studies conducted in Asia (AUC = .944) demonstrated higher accuracy compared to studies in Western countries (AUC = .820) and Korea (AUC = .864). Additional analysis of the moderating effects in domestic research indicated that a higher proportion of males and the prediction of suicide attempts were associated with higher prediction accuracy. On the other hand, prediction accuracy was lower when the prediction target was suicide deaths and when studies utilized neural network analysis. This study synthesized various research findings on the prediction of suicidal behaviors, verified the effectiveness of prediction using machine learning, and holds significance in exploring variables applicable in the context of South Korea.

• Journal of Korean Academy of Nursing
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• v.51 no.1
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• pp.40-53
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• 2021

Purpose: The purpose of this study was to develop and compare the prediction model for suicide attempts by Korean adolescents using logistic regression and decision tree analysis. Methods: This study utilized secondary data drawn from the 2019 Youth Health Risk Behavior web-based survey. A total of 20 items were selected as the explanatory variables (5 of sociodemographic characteristics, 10 of health-related behaviors, and 5 of psychosocial characteristics). For data analysis, descriptive statistics and logistic regression with complex samples and decision tree analysis were performed using IBM SPSS ver. 25.0 and Stata ver. 16.0. Results: A total of 1,731 participants (3.0%) out of 57,303 responded that they had attempted suicide. The most significant predictors of suicide attempts as determined using the logistic regression model were experience of sadness and hopelessness, substance abuse, and violent victimization. Girls who have experience of sadness and hopelessness, and experience of substance abuse have been identified as the most vulnerable group in suicide attempts in the decision tree model. Conclusion: Experiences of sadness and hopelessness, experiences of substance abuse, and experiences of violent victimization are the common major predictors of suicide attempts in both logistic regression and decision tree models, and the predict rates of both models were similar. We suggest to provide programs considering combination of high-risk predictors for adolescents to prevent suicide attempt.

• Archives of Plastic Surgery
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• v.42 no.2
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• pp.131-142
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• 2015

Literature indicates an increased risk of suicide among women who have had cosmetic breast implants. An explanatory model for this association has not been established. Some studies conclude that women with cosmetic breast implants demonstrate some characteristics that are associated with increased suicide risk while others support that the breast augmentation protects from suicide. A systematic review including data collection from January 1961 up to February 2014 was conducted. The results were incorporated to pre-existing suicide risk models of the general population. A modified suicide risk model was created for the female cosmetic augmentation mammaplasty candidate. A 2-3 times increased suicide risk among women that undergo cosmetic breast augmentation has been identified. Breast augmentation patients show some characteristics that are associated with increased suicide risk. The majority of women reported high postoperative satisfaction. Recent research indicates that the Autoimmune syndrome induced by adjuvants and fibromyalgia syndrome are associated with silicone implantation. A thorough surgical, medical and psycho-social (psychiatric, family, reproductive, and occupational) history should be included in the preoperative assessment of women seeking to undergo cosmetic breast augmentation. Breast augmentation surgery can stimulate a systematic stress response and increase the risk of suicide. Each risk factor of suicide has poor predictive value when considered independently and can result in prediction errors. A clinical management model has been proposed considering the overlapping risk factors of women that undergo cosmetic breast augmentation with suicide.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.35 no.1
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• pp.75-81
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• 2024

Objectives: Although adolescents appear less vulnerable to coronavirus disease (COVID-19), the side effects of this pandemic can still be devastating. Bullying and suicidality are significant global issues with detrimental effects on young people, particularly during school closure. This study aimed to identify the relationship between bullying and suicide risk among adolescents in Indonesia during the COVID-19 pandemic. Methods: A cross-sectional study was conducted on adolescents aged 14-18 years in May 2020 in Bandung, Indonesia, using a web-based closed survey. The Adolescent Peer Relations Instrument and the Suicide Behavior Questionnaire-Revised were used to measure bullying and risk of suicide. Multinomial logistic regression analysis was performed. Results: This study included 268 participants in 2020 and 175 participants in 2019. In 2020, the prevalence of perpetrators and victims of bullying combined was 74.6%. Meanwhile, in 2019, the prevalence of perpetrators and victims of bullying combined was 82.9%. Risk of suicide increased from 26.1% in 2019 (before the COVID-19 pandemic) to 36.5% in 2020 (during the first wave of the COVID-19 pandemic). The risk of perpetrators and suicide victims was higher than that of perpetrators and victims alone (odds ratio [OR]=4.0, 95% confidence interval [CI]=1.5-6.6 vs. OR=1.3, 95% CI=1.0-2.9 and OR=1.6, 95% CI=1.1-2.8, respectively). Conclusion: Bullying can enhance the likelihood of suicide among adolescents in Indonesia, and the risk was highest for the combination of victims and perpetrators. It is very important to provide early risk prediction for youths with bullying behavior and improve the knowledge and understanding of families and schools regarding the negative effects of bullying behavior.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.4
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• pp.1863-1870
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• 2013

The purpose of this study is when the cases will be found, used as a basic data for clinical severity prediction, and research on suicide prevention. By classifying the group of survival and death about the patients who visit the Emergency Medical Center by attempt suicide by drug addiction, identifying the condition when visiting and results of the treatment after visiting. From June 2009 to May 2011, last two years data that among the drug abusers who visited the Emergency Medical Center in C-University Hospital in Gwang-Ju, only suicidal patients, except with unintentional accidents were collected. The findings, among the drug addiction patients who high age, lower level of education and living alone were the mortality rate was higher. And if who drunk the agricultural chemicals, the convalescence was not good. If the causes of suicide were economic problems and depression, the mortality rate was higher. And when visit hospital, if the consciousness was stupor and semi-coma/coma, the convalescence was not good. As grasp the risk for suicide patients of drug addiction, help on the Prediction of clinical severity, also stamp the appropriate drug education with psychological support is more important on them.

• The Journal of Information Systems
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• v.28 no.3
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• pp.249-276
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• 2019

Purpose The purpose of this study is to develop a prediction model and decision rules for the elderly's suicidal ideation based on the Korean Welfare Panel survey data. By utilizing this data, we obtained many decision rules to predict the elderly's suicide ideation. Design/methodology/approach This study used classification analysis to derive decision rules to predict on the basis of decision tree technique. Weka 3.8 is used as the data mining tool in this study. The decision tree algorithm uses J48, also known as C4.5. In addition, 66.6% of the total data was divided into learning data and verification data. We considered all possible variables based on previous studies in predicting suicidal ideation of the elderly. Finally, 99 variables including the target variable were used. Classification analysis was performed by introducing sampling technique through backward elimination and data balancing. Findings As a result, there were significant differences between the data sets. The selected data sets have different, various decision tree and several rules. Based on the decision tree method, we derived the rules for suicide prevention. The decision tree derives not only the rules for the suicidal ideation of the depressed group, but also the rules for the suicidal ideation of the non-depressed group. In addition, in developing the predictive model, the problem of over-fitting due to the data imbalance phenomenon was directly identified through the application of data balancing. We could conclude that it is necessary to balance the data on the target variables in order to perform the correct classification analysis without over-fitting. In addition, although data balancing is applied, it is shown that performance is not inferior in prediction rate when compared with a biased prediction model.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.34 no.4
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• pp.250-257
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• 2023

Objectives: Adolescents are at risk of suicide. As suicide is a multifactorial process, risk and protective factors are relevant constructs for suicide prediction. This study explored the effects of risk and protective factors on suicidal ideation in adolescents on the island of São Miguel (Azores). Methods: A sample of 750 adolescents (male: n=358; 47.7%; mean age=14.67 years; standard deviation=1.85 years) from the island of São Miguel (Azores) completed several measures related to suicidal ideation and associated factors. Using a cross-sectional design, this study conducted descriptive, correlational, predictive, mediation, and moderation analyses. Results: Adolescents generally displayed high levels of risk and protective factors; an indicative proportion exhibited significant suicidal ideation with females presenting the greatest vulnerability. Furthermore, the results highlight that depression is the best predictor of suicidal ideation, however, the association between these variables is mediated. Conclusion: The data corroborate that the suicidal reality of adolescents in the Autonomous Region of the Azores is worrisome. Having substantiated the complexity of the suicidal context in young people in the present research, the need to continue studying risk/protective factors in this area is supported.