Citation

MA-Ming LIUHLIUR. Chin. J. Sch. Health 2022; (12): 763-767.

Copyright

(Copyright © 2022, Zhongguo xue xiao wei sheng za zhi she)

DOI

unavailable

PMID

unavailable

Abstract

OBJECTIVE@#To explore the predictive effect of machine learning algorithms on college students suicidal ideation and to analyze the associated factors of college students suicidal ideation.@*Methods@#The mental health data of 21 224 undergraduates was selected from a university in 2021. The independent variables were 37 demographic and internal and external mental health factors. The dependent variable was whether college students had suicidal ideation. Support vector machine, random forest and LightGBM algorithm were used to establish prediction models. The model was used in test set to so as to evaluate the model s prediction effect by using detection rate, F1 score and accuracy rate. Based on the superior model, the highrisk factors of suicidal ideation in college students were analyzed.@*Results@#The detection rates of support vector machine, random forest, and LightGBM models were 61.0% ,64.0%, 69.0%; F1 scores were 0.63, 0.63, 0.64, and accuracy rates were 73.0%, 73.0%, 72.0%, respectively. Based on the superior LightGBM model, risk factors of suicidal ideation in college students included, depression, grade, gender, despair, place of origin, sense of meaning, attitude toward suicide, dependence, family economic situation, hallucinatory delusion symptoms, anxiety, internet addiction, and interpersonal distress.@*Conclusion@#The LightGBM model has a better prediction effect than the support vector machine and random forest models.

Language: zh

Keywords

Students; Suicide; Mental health; Consciousness; Models,statistical