JMIR formative research · 2025
Factors Associated With Suicidal Ideation Among Persons With Disabilities in South Korea: Retrospective Observational Study.
СтатьяНаблюдательное исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Oh In-Hwan, Jo Sooyeon, Lee Joon
Аннотация
BACKGROUND: South Korea has the highest suicide rate among the Organisation for Economic Co-operation and Development nations, with particularly elevated figures among persons with disabilities. Research has shown a strong correlation between suicidal ideation and suicide attempts.
OBJECTIVE: This study aimed to investigate the factors that contribute to suicidal ideation among persons with disabilities in South Korea, utilizing machine learning methods based on national survey data.
METHODS: We employed data from the 2020 National Survey on Persons with Disabilities in South Korea, which included 7025 respondents. The primary variable of interest was the answer to the question, "have you thought about taking your own life in the past year?" The dataset was divided into training (80%) and test (20%) subsets. Because the survey contained too many questions (n=1394), feature selection was conducted using random forest variable importance to identify the top 100 features. Subsequently, 5 machine learning models were trained to predict suicidal ideation based on the selected features: logistic regression, support vector machine, random forest, Extreme Gradient Boosting (XGBoost), and feed-forward neural network.
RESULTS: A total of 6832 persons with disabilities responded to the suicidal ideation question and were included in the study. The most common types of primary disability were physical disability (n=1773, 26.0%) and hearing disability (n=979, 14.3%). Of the 6832 persons with disabilities, 12.1% (n=829) indicated they had had suicidal thoughts in the past year. Significant factors that impacted suicidal ideation included intense feelings of sadness, difficulties associated with their disabilities, and overall health satisfaction. Among the models tested, the random forest model exhibited the best predictive performance with a median area under the receiver operating characteristic curve of 0.905 (IQR 0.895-0.913), a median precision of 0.592 (IQR 0.561-0.616), and a median recall of 0.588 (IQR 0.564-0.620).
CONCLUSIONS: This study highlights critical predictors of suicidal ideation in persons with disabilities in South Korea, underscoring the necessity for focused mental health interventions. The results demonstrate the potential of machine learning to identify these factors, which can aid in the development of future suicide prevention strategies. Future work is warranted to investigate if the factors identified in this study are causal.
OBJECTIVE: This study aimed to investigate the factors that contribute to suicidal ideation among persons with disabilities in South Korea, utilizing machine learning methods based on national survey data.
METHODS: We employed data from the 2020 National Survey on Persons with Disabilities in South Korea, which included 7025 respondents. The primary variable of interest was the answer to the question, "have you thought about taking your own life in the past year?" The dataset was divided into training (80%) and test (20%) subsets. Because the survey contained too many questions (n=1394), feature selection was conducted using random forest variable importance to identify the top 100 features. Subsequently, 5 machine learning models were trained to predict suicidal ideation based on the selected features: logistic regression, support vector machine, random forest, Extreme Gradient Boosting (XGBoost), and feed-forward neural network.
RESULTS: A total of 6832 persons with disabilities responded to the suicidal ideation question and were included in the study. The most common types of primary disability were physical disability (n=1773, 26.0%) and hearing disability (n=979, 14.3%). Of the 6832 persons with disabilities, 12.1% (n=829) indicated they had had suicidal thoughts in the past year. Significant factors that impacted suicidal ideation included intense feelings of sadness, difficulties associated with their disabilities, and overall health satisfaction. Among the models tested, the random forest model exhibited the best predictive performance with a median area under the receiver operating characteristic curve of 0.905 (IQR 0.895-0.913), a median precision of 0.592 (IQR 0.561-0.616), and a median recall of 0.588 (IQR 0.564-0.620).
CONCLUSIONS: This study highlights critical predictors of suicidal ideation in persons with disabilities in South Korea, underscoring the necessity for focused mental health interventions. The results demonstrate the potential of machine learning to identify these factors, which can aid in the development of future suicide prevention strategies. Future work is warranted to investigate if the factors identified in this study are causal.