Use of a Machine Learning Algorithm to Predict Individuals with Suicide Ideation in the General Population

• 영문명: Use of a Machine Learning Algorithm to Predict Individuals with Suicide Ideation in the General Population
• 발행기관: 대한신경정신의학회
• 저자명: Seung-hyong Ryu Hyeong-rae Lee Dong-Kyun Lee Kyeong-woo Park
• 간행물 정보: 『Psychiatry Investigation』제15권 제11호, 1030~1036쪽, 전체 7쪽
• 주제분류: 의약학 > 정신과학
• 파일형태: PDF
• 발행일자: 2018.11.30

영문 초록

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.

목차

INTRODUCTION

METHODS

RESULTS

DISCUSSION

키워드

Suicide ideation Prediction Machine learning algorithm Public health data