Detection of Suicide Attempters among Suicide Ideators Using Machine Learning
- Author:
Seunghyong RYU
1
;
Hyeongrae LEE
;
Dong Kyun LEE
;
Sung Wan KIM
;
Chul Eung KIM
Author Information
- Publication Type:Original Article
- Keywords: Suicide attempt; Suicide ideation; Machine learning; Public health data
- MeSH: Forests; Korea; Machine Learning; Risk Factors; ROC Curve; Suicide
- From:Psychiatry Investigation 2019;16(8):588-593
- CountryRepublic of Korea
- Language:English
- Abstract: OBJECTIVE: We aimed to develop predictive models to identify suicide attempters among individuals with suicide ideation using a machine learning algorithm. METHODS: 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. RESULTS: 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%. CONCLUSION: 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.
