Application of machine learning in the prediction of college students' suicidal ideation

MA Ming; LIU Huan; LIU Runxiang

doi:10.16835/j.cnki.1000-9817.2022.05.029

Volume 43 Issue 5

May 2022

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Article Navigation > CHINESE JOURNAL OF SCHOOL HEALTH > 2022 > 43(5): 763-767

MA Ming, LIU Huan, LIU Runxiang. Application of machine learning in the prediction of college students' suicidal ideation[J]. CHINESE JOURNAL OF SCHOOL HEALTH, 2022, 43(5): 763-767. doi: 10.16835/j.cnki.1000-9817.2022.05.029

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MA Ming, LIU Huan, LIU Runxiang. Application of machine learning in the prediction of college students' suicidal ideation[J]. CHINESE JOURNAL OF SCHOOL HEALTH, 2022, 43(5): 763-767. doi: 10.16835/j.cnki.1000-9817.2022.05.029

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Application of machine learning in the prediction of college students' suicidal ideation

doi: 10.16835/j.cnki.1000-9817.2022.05.029

MA Ming¹,
LIU Huan¹,
LIU Runxiang^{1, 2
,
,}

1.
Department of Psychology, School of Public Administration, Nanchang University, Nanchang(330031), China

Received Date: 2022-01-26
Rev Recd Date: 2022-04-06

Available Online: 2022-05-20

Publish Date: 2022-05-25

Abstract

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.
- Suicide,
- Consciousness,
- Mental health,
- Models, statistical,
- Students

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References(33)

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