Department of Health Administration, Graduate School, Yonsei University, Wonju, Republic of Korea.
Yonsei Institute of Health and Welfare, Yonsei University Mirae Campus, Wonju, Republic of Korea.
PLoS One. 2024 Jul 22;19(7):e0305777. doi: 10.1371/journal.pone.0305777. eCollection 2024.
Suicide among the older population is a significant public health concern in South Korea. As the older individuals have long considered suicide before committing suicide trials, it is important to analyze the suicidal ideation that precedes the suicide attempt for intervention. In this study, six machine learning algorithms were employed to construct a predictive model for suicidal thinking and identify key variables. A traditional logistic regression analysis was supplementarily conducted to test the robustness of the results of machine learning. All analyses were conducted using a hierarchical approach to compare the model fit of each model in both machine learning and logistic regression. Three models were established for analysis. In Model 1, socioeconomic, residential, and health behavioral factors were incorporated. Model 2 expanded upon Model 1 by integrating physical health status, and Model 3 further incorporated mental health conditions. The results indicated that the gradient boosting algorithm outperformed the other machine learning techniques. Furthermore, the household income quintile was the most important feature in Model 1, followed by subjective health status, oral health, and exercise ability in Model 2, and anxiety and depression in Model 3. These results correspond to those of the hierarchical logistic regression. Notably, economic and residential vulnerabilities are significant factors in the mental health of the older population with higher instances of suicidal thoughts. This hierarchical approach could reveal the potential target population for suicide interventions.
韩国老年人自杀是一个严重的公共卫生问题。由于老年人在自杀尝试前往往已经有过自杀念头，因此分析自杀企图前的自杀意念对于干预至关重要。本研究采用六种机器学习算法构建了自杀思维预测模型，并识别关键变量。同时还进行了传统的逻辑回归分析，以检验机器学习结果的稳健性。所有分析均采用分层方法进行，以比较机器学习和逻辑回归中每个模型的模型拟合度。建立了三个模型进行分析。在模型 1 中，纳入了社会经济、居住和健康行为因素。模型 2 在模型 1 的基础上扩展了身体健康状况，而模型 3 进一步纳入了心理健康状况。结果表明，梯度提升算法优于其他机器学习技术。此外，在模型 1 中，家庭收入五分位数是最重要的特征，其次是主观健康状况、口腔健康和运动能力，而在模型 2 中则是焦虑和抑郁，在模型 3 中也是如此。这些结果与分层逻辑回归的结果一致。值得注意的是，经济和居住方面的脆弱性是具有较高自杀意念的老年人群心理健康的重要因素。这种分层方法可以揭示自杀干预的潜在目标人群。
