Predictors of suicide ideation among South Korean adolescents: A machine learning approach

Abstract

Background: The current study developed a predictive model for suicide ideation among South Korean (Korean) adolescents using a comprehensive set of factors across demographic, physical and mental health, academic, social, and behavioral domains. The aim of this study was to address the pressing public health concerns of adolescent suicide in Korea and the methodological limitations of suicidal research.

Methods: This study used machine learning methods (decision tree, logistic regression, naive Bayes classifier) to improve the accuracy of predicting suicidal ideation and related factors among a nationally representative sample of Korean middle school students (N = 6666).

Results: Factors within all domains, including demographic characteristics, physical and mental health, and academic, social, and behavioral, were important in predicting suicidal thoughts among Korean adolescents, with mental health being the most important factor.

Limitations: The predictive model of the current research does not infer causality, and there may have been some loss of information due to measurement issues.

Conclusions: Study results provide insights for taking a multidimensional approach when identifying adolescents at risk of suicide, which may be used to further address their needs through intervention programs within the school setting. Considering the cultural stigma attached to disclosing suicidal ideation and behavior, the current study proposes the need for a preventive screening process based on the observation and assessment of adolescents' general characteristics and experiences in everyday life.

Keywords: Adolescents; Machine learning; Screening; South Korea; Suicide.