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ACOMS+ 및 학술지 리포지터리 설명회

  • 한국과학기술정보연구원(KISTI) 서울분원 대회의실(별관 3층)
  • 2024년 07월 03일(수) 13:30
 

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Analysis of Adolescent Suicide Factors based on Random Forest Machine Learning Algorithm

인공지능연구 / Korean Journal of Artificial Intelligence, (E)2508-7894
2023, v.11 no.3, pp.23-27
https://doi.org/https://doi.org/10.24225/kjai.2023.11.3.23
Gi-Lim HA (Dept. of Medical IT, Eulji University)
In Seon EO (Sysone)
Dong Hun HAN (Dept. of Medical Intelligence, Eulji University)
Min Soo KANG (Dept. of Medical IT, Eulji University)

Abstract

The purpose of this study is to identify and analyze suicide factors of adolescents using the Random Forest algorithm. According to statistics on the cause of death by the National Statistical Office in 2019, suicide was the highest cause of death in the 10-19 age group, which is a major social problem. Using machine learning algorithms, research can predict whether individual adolescents think of suicide without investigating suicidal ideation and can contribute to protecting adolescents and analyzing factors that affect suicide, establishing effective intervention measures. As a result of predicting with the random forest algorithm, it can be said that the possibility of identifying and predicting suicide factors of adolescents was confirmed. To increase the accuracy of the results, continuous research on the factors that induce youth suicide is necessary.

keywords
Suicide Factors, Adolescent, Random Forest Algorithm, Machine learning

인공지능연구