Detection of Suicide Attempters among Suicide Ideators Using Machine Learning

• Published in: Psychiatry investigation Vol. 16; no. 8; pp. 588 - 593
• Main Authors: Ryu, Seunghyong, Lee, Hyeongrae, Lee, Dong-Kyun, Kim, Sung-Wan, Kim, Chul-Eung
• Format: Journal Article
• Language: English
• Published: Korea (South) Korean Neuropsychiatric Association 01.08.2019; 대한신경정신의학회
• Subjects: Original; 정신과학; Public health data; Suicide ideation; Machine learning; Suicide attempt
• ISSN: 1738-3684; 1976-3026
• DOI: 10.30773/pi.2019.06.19

We aimed to develop predictive models to identify suicide attempters among individuals with suicide ideation using a machine learning algorithm. Among 35,116 individuals aged over 19 years from the Korea National Health & Nutrition Examination Survey, we selected 5,773 subjects who reported experiencing suicide ideation and had answered a survey question about suicide attempts. Then, we performed resampling with the Synthetic Minority Over-sampling TEchnique (SMOTE) to obtain data corresponding to 1,324 suicide attempters and 1,330 non-suicide attempters. We randomly assigned the samples to a training set (n=1,858) and a test set (n=796). In the training set, random forest models were trained with features selected through recursive feature elimination with 10-fold cross validation. Subsequently, the fitted model was used to predict suicide attempters in the test set. In the test set, the prediction model achieved very good performance [area under receiver operating characteristic curve (AUC)=0.947] with an accuracy of 88.9%. Our results suggest that a machine learning approach can enable the prediction of individuals at high risk of suicide through the integrated analysis of various suicide risk factors.