Detection of Suicide Attempters among Suicide Ideators Using Machine Learning
Psychiatry Investigation
; : 588-593, 2019.
Article
in En
| WPRIM
| ID: wpr-760972
Responsible library:
WPRO
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.
Key words
Full text:
1
Database:
WPRIM
Main subject:
Suicide
/
Forests
/
Risk Factors
/
ROC Curve
/
Machine Learning
/
Korea
Type of study:
Diagnostic_studies
/
Etiology_studies
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Prognostic_studies
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Risk_factors_studies
Country/Region as subject:
Asia
Language:
En
Journal:
Psychiatry Investigation
Year:
2019
Document type:
Article