Identification of Risk Factors for Suicidal Ideation and Attempt Based on Machine Learning Algorithms: A Longitudinal Survey in Korea (2007–2019)

Junggu Choi,Inhwan Ko,Sanghoon Han,Seoyoung Cho

doi:10.3390/ijerph182312772

Abstract

Investigating suicide risk factors is critical for socioeconomic and public health, and many researchers have tried to identify factors associated with suicide. In this study, the risk factors for suicidal ideation were compared, and the contributions of different factors to suicidal ideation and attempt were investigated. To reflect the diverse characteristics of the population, the large-scale and longitudinal dataset used in this study included both socioeconomic and clinical variables collected from the Korean public. Three machine learning algorithms (XGBoost classifier, support vector classifier, and logistic regression) were used to detect the risk factors for both suicidal ideation and attempt. The importance of the variables was determined using the model with the best classification performance. In addition, a novel risk-factor score, calculated from the rank and importance scores of each variable, was proposed. Socioeconomic and sociodemographic factors showed a high correlation with risks for both ideation and attempt. Mental health variables ranked higher than other factors in suicidal attempts, posing a relatively higher suicide risk than ideation. These trends were further validated using the conditions from the integrated and yearly dataset. This study provides novel insights into suicidal risk factors for suicidal ideations and attempts.

Highlights

We applied machine learning algorithms to identify risk factors for suicidal ideation and attempts using longitudinal datasets collected from the general population living in Korea
To compare the differences between suicidal ideation and suicidal attempt factors, the KNHANES dataset was preprocessed for two dependent variables (‘BP6_100 : suicide ideation and ‘BP6_310 : suicide attempt)
To confirm the optimized machine learning algorithms for our research topic, we compared the performances of three machine learning classifiers (XGBoost classifier, support vector classifier, and logistic regression)

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. In the past 10 years, approximately 800,000 people committed suicide annually [1,2,3]. Suicidal mortality is considered a critical factor for both social and public health [4,5,6]. Many previous studies have suggested that death by suicide has socioeconomic and psychological consequences, burdening members of society [7,8,9]. Paul et al [10] analyzed the social and economic burden of suicide in the Hong Kong SAR. Shumona et al [11]

Methods

Results

Discussion

Conclusion