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

Author

Listed:
  • Junggu Choi

    (Yonsei Graduate Program in Cognitive Science, Yonsei University, Seoul 03722, Korea
    These authors contributed equally.)

  • Seoyoung Cho

    (Yonsei Graduate Program in Cognitive Science, Yonsei University, Seoul 03722, Korea
    These authors contributed equally.)

  • Inhwan Ko

    (Department of Psychology, Yonsei University, Seoul 03722, Korea)

  • Sanghoon Han

    (Yonsei Graduate Program in Cognitive Science, Yonsei University, Seoul 03722, Korea
    Department of Psychology, Yonsei University, Seoul 03722, Korea)

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.

Suggested Citation

  • Junggu Choi & Seoyoung Cho & Inhwan Ko & Sanghoon Han, 2021. "Identification of Risk Factors for Suicidal Ideation and Attempt Based on Machine Learning Algorithms: A Longitudinal Survey in Korea (2007–2019)," IJERPH, MDPI, vol. 18(23), pages 1-21, December.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:23:p:12772-:d:694642
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    References listed on IDEAS

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    1. Shumona Sharmin Salam & Olakunle Alonge & Md Irteja Islam & Dewan Md Emdadul Hoque & Shirin Wadhwaniya & Md Kamran Ul Baset & Saidur Rahman Mashreky & Shams El Arifeen, 2017. "The Burden of Suicide in Rural Bangladesh: Magnitude and Risk Factors," IJERPH, MDPI, vol. 14(9), pages 1-16, September.
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