TY  - JOUR
PY  - 2019//
TI  - Detection of suicide attempters among suicide ideators using machine learning
JO  - Psychiatry investigation
A1  - Ryu, Seunghyong
A1  - Lee, Hyeongrae
A1  - Lee, Dong-Kyun
A1  - Kim, Sung-Wan
A1  - Kim, Chul-Eung
SP  - 588
EP  - 593
VL  - 16
IS  - 8
N2  - OBJECTIVE: We aimed to develop predictive models to identify suicide attempters among individuals with suicide ideation using a machine learning algorithm. <br><br>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. <br><br>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%. <br><br>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.<p />  <p>Language: en</p>
LA  - en
SN  - 1738-3684
UR  - http://dx.doi.org/10.30773/pi.2019.06.19
ID  - ref1
ER  -