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KMID : 1138720240500010017
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Korean Public Health Research
2024 Volume.50 No. 1 p.17 ~ p.35
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Predicting and Analyzing Suicidal Ideations in Middle and Older Adults: A Hybrid Study of Machine Learning and Traditional Statistical Methods
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Jung Hyun-Woo
Jang Jin-Su
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Abstract
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Purpose: This study aims to develop a predictive model and identify crucial variables associated with suicidal ideation, employing factor analysis.
Methods: Distinct models were created for both the overall and older adult population. Six machine learning algorithms were applied to construct predictive models and assess feature importance. Traditional logistic regression analysis was conducted for factor analysis.
Results: Gradient Boosting and SVM stood out, highlighting anxiety and depression as pivotal variables. For the older adults, anxiety & depression, future anxiety, and being a care recipient were crucial features. Logistic regression analysis indicated the significance of mental and physical health, along with residential factors.
Conclusion: The machine learning results closely aligned with outcomes from traditional statistical models, showcasing generally reliable findings. Suicidal ideation appears linked to situations that are challenging to overcome through individual efforts alone. This study emphasizes the necessity for cultural and policy programs fostering inclusivity throughout South Korea.
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KEYWORD
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Older adults, Suicidal thinking, Machine learning, Feature importance
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