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Development of a Suicide Prediction Model for the Elderly Using Health Screening Data

Author

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  • Seo-Eun Cho

    (Department of Psychiatry, Gachon University College of Medicine, Gil Medical Center, Incheon 21565, Korea)

  • Zong Woo Geem

    (College of IT Convergence, Gachon University, Seongnam 13120, Korea)

  • Kyoung-Sae Na

    (Department of Psychiatry, Gachon University College of Medicine, Gil Medical Center, Incheon 21565, Korea)

Abstract

Suicide poses a serious problem globally, especially among the elderly population. To tackle the issue, this study aimed to develop a model for predicting suicide by using machine learning based on the elderly population. To obtain a large sample, the study used the big data health screening cohort provided by the National Health Insurance Sharing Service. By applying a machine learning technique, a predictive model that comprehensively utilized various factors was developed to select the elderly aged > 65 years at risk of suicide. A total of 48,047 subjects were included in the analysis. Individuals who died by suicide were older, and the number of men was significantly greater. The suicide group had a more prominent history of depression, with the use of medicaments significantly higher. Specifically, the prescription of benzodiazepines alone was associated with a high suicide risk. Furthermore, body mass index, waist circumference, total cholesterol, and low-density lipoprotein level were lower in the suicide group. We developed a model for predicting suicide by using machine learning based on the elderly population. This suicide prediction model can satisfy the performance to some extent by employing only the medical service usage behavior without subjective reports.

Suggested Citation

  • Seo-Eun Cho & Zong Woo Geem & Kyoung-Sae Na, 2021. "Development of a Suicide Prediction Model for the Elderly Using Health Screening Data," IJERPH, MDPI, vol. 18(19), pages 1-10, September.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:19:p:10150-:d:644453
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    References listed on IDEAS

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    1. Matthew Large & Muthusamy Kaneson & Nicholas Myles & Hannah Myles & Pramudie Gunaratne & Christopher Ryan, 2016. "Meta-Analysis of Longitudinal Cohort Studies of Suicide Risk Assessment among Psychiatric Patients: Heterogeneity in Results and Lack of Improvement over Time," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-17, June.
    2. Young Ko & Song Yi Han & Hye-Young Jang, 2021. "Factors Influencing Suicidal Ideation and Attempts among Older Korean Adults: Focusing on Age Discrimination and Neglect," IJERPH, MDPI, vol. 18(4), pages 1-13, February.
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    Cited by:

    1. Rosa Gravagnuolo & Stefano Tambuzzi & Guendalina Gentile & Michele Boracchi & Franca Crippa & Fabio Madeddu & Riccardo Zoja & Raffaella Calati, 2023. "Is It Correct to Consider Caustic Ingestion as a Nonviolent Method of Suicide? A Retrospective Analysis and Psychological Considerations," IJERPH, MDPI, vol. 20(13), pages 1-18, June.

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