@article{ref1,
title="Use of a machine learning algorithm to predict individuals with suicide ideation in the general population",
journal="Psychiatry investigation",
year="2018",
author="Ryu, Seunghyong and Lee, Hyeongrae and Lee, Dong-Kyun and Park, Kyeongwoo",
volume="15",
number="11",
pages="1030-1036",
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. <br><br>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. <br><br>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. <br><br>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.<p /> <p>Language: en</p>",
language="en",
issn="1738-3684",
doi="10.30773/pi.2018.08.27",
url="http://dx.doi.org/10.30773/pi.2018.08.27"
}