Use of a Machine Learning Algorithm to Predict Individuals with Suicide Ideation in the General Population
Psychiatry Investigation
;
: 1030-1036, 2018.
Article
Dans Anglais
| 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.
Texte intégral:
Disponible
Indice:
WPRIM (Pacifique occidental)
Sujet Principal:
Suicide
/
Forêts
/
Dépistage de masse
/
Courbe ROC
/
Sensibilité et spécificité
/
Apprentissage machine
/
Corée
Type d'étude:
Etude diagnostique
/
Étude pronostique
/
Étude de dépistage
Pays comme sujet:
Asie
langue:
Anglais
Texte intégral:
Psychiatry Investigation
Année:
2018
Type:
Article
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