Psychiatry Investig Search

CLOSE


Psychiatry Investig > Epub ahead of print
[Epub ahead of print]
DOI: https://doi.org/10.30773/pi.2018.08.27    Published online October 11, 2018.
Use of a Machine Learning Algorithm to Predict Individuals with Suicide Ideation in the General Population
Seunghyong Ryu, Hyeongrae Lee, Dong-Kyun Lee, Kyeongwoo Park
Department of Mental Health Research, National Center for Mental Health, Seoul, Republic of Korea
Correspondence: Seunghyong Ryu ,Tel: +82-2-2204-0109, Fax: +82-2-2204-0393, Email: seunghyongryu@gmail.com
Received: June 24, 2018   Accepted: August 27, 2018   Published online: October 11, 2018
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.
Key words   Suicide ideation, Prediction, Machine learning algorithm, Public health data
TOOLS
Share:
Facebook Twitter Linked In Google+
METRICS Graph View
  • 0 Crossref
  •   Scopus
  • 82 View
  • 13 Download


ABOUT
AUTHOR INFORMATION
ARTICLE CATEGORY

Browse all articles >

BROWSE ARTICLES
Editorial Office
#522, 27, Seochojungang-ro 24-gil, Seocho-gu, Seoul 06601, Korea
Tel: +82-2-537-6171    Fax: +82-2-537-6174    E-mail: knpa1945@hanmail.net                

Copyright © 2018 by Korean Neuropsychiatric Association. All rights reserved.

Developed in M2community

Close layer
prev next