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Abstract
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BACKGROUND: Korea has the highest suicide rate among Organisation for Economic Co-operation and Development (OECD) countries. Consequently, central and local governments and private organizations in Korea cooperate in promoting various suicide prevention projects to actively respond to suicide problems. Machine learning has been used to predict suicidal ideation in the fields of health and medicine but not from a social science perspective.
OBJECTIVE: Since suicidal ideation is a major predictor of suicide attempts, being able to anticipate and mitigate it helps prevent suicide. Therefore, this study presents a data-based analysis method for predicting suicidal thoughts quickly and effectively and suggests countermeasures against the causes of suicidal thoughts. PARTICIPANTS AND METHODS: To predict early signs of suicidal ideation in children and adolescents, big data collected for approximately 4 years (from 2017 to 2020) from the Korea Youth Policy Institute (NYPI) were used. To accurately predict suicidal ideation, supervised ma- chine learning classification algorithms such as logistic regression, random forest, XGBoost, multilayer perceptron (MLP), and convolutional neural network (CNN) were used.
RESULTS: Using CNN, suicidal ideation was predicted with an accuracy of approximately 90 %. The logistic regression results showed that sadness and depression increased suicidal thoughts by more than 25 times, and anxiety, loneliness, and experience of abusive language increased suicidal thoughts by more than three times.
CONCLUSIONS: Machine learning and deep learning approaches have the potential to predict and respond to suicidal thoughts in children, adolescents, and the general population, as well as help respond to the suicide crisis by preemptively identifying the cause.
Language: en |