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.
Funder
Yonsei Signature Research Cluster Program of 2021
Subject
Health, Toxicology and Mutagenesis,Public Health, Environmental and Occupational Health
Reference49 articles.
1. Suicide Worldwide in 2019https://www.who.int/publications/i/item/9789240026643
2. Suicide in the Worldhttps://www.who.int/publications/i/item/suicide-in-the-world
3. Suicide in the World: Global Health Estimateshttps://apps.who.int/iris/handle/10665/326948
4. Suicide mortality in the European Union
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