Use of a Machine Learning Algorithm to Predict Individuals with Suicide Ideation in the General Population
Psychiatry Investigation
;
: 1030-1036, 2018.
Article
in English
| WPRIM
| ID: wpr-718244
ABSTRACT
OBJECTIVE:
In this study, we aimed to develop a model predicting individuals with suicide ideation within a general population using a machine learning algorithm.METHODS:
Among 35,116 individuals aged over 19 years from the Korea National Health & Nutrition Examination Survey, we selected 11,628 individuals via random down-sampling. This included 5,814 suicide ideators and the same number of non-suicide ideators. We randomly assigned the subjects to a training set (n=10,466) and a test set (n=1,162). In the training set, a random forest model was trained with 15 features selected with recursive feature elimination via 10-fold cross validation. Subsequently, the fitted model was used to predict suicide ideators in the test set and among the total of 35,116 subjects. All analyses were conducted in R.RESULTS:
The prediction model achieved a good performance [area under receiver operating characteristic curve (AUC)=0.85] in the test set and predicted suicide ideators among the total samples with an accuracy of 0.821, sensitivity of 0.836, and specificity of 0.807.CONCLUSION:
This study shows the possibility that a machine learning approach can enable screening for suicide risk in the general population. Further work is warranted to increase the accuracy of prediction.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Suicide
/
Forests
/
Mass Screening
/
ROC Curve
/
Sensitivity and Specificity
/
Machine Learning
/
Korea
Type of study:
Diagnostic study
/
Prognostic study
/
Screening study
Country/Region as subject:
Asia
Language:
English
Journal:
Psychiatry Investigation
Year:
2018
Type:
Article
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