Constructing prediction models and analyzing factors in suicidal ideation using machine learning, focusing on the older population

• Published in: PloS one Vol. 19; no. 7; p. e0305777
• Main Authors: Jung, Hyun Woo, Jang, Jin Su
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 22.07.2024; Public Library of Science (PLoS)
• Subjects: Aged; Aged, 80 and over; Algorithms; Analysis; Biology and Life Sciences; Computer and Information Sciences; Data mining; Depression, Mental; Female; Health behavior; Health Status; Humans; Learning algorithms; Logistic Models; Machine Learning; Male; Medicine and Health Sciences; Mental depression; Mental disorders; Mental health; Mental Health - statistics & numerical data; Middle Aged; Physical Sciences; Population studies; Prediction models; Predictions; Public health; Regression analysis; Regression models; Republic of Korea - epidemiology; Research and Analysis Methods; Risk Factors; Social Sciences; Socioeconomic Factors; Suicidal behavior; Suicidal Ideation; Suicide; Suicide, Attempted - psychology; Suicide, Attempted - statistics & numerical data; Suicides & suicide attempts; Republic of Korea; South Korea
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0305777

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.