Influencing factors analysis and prediction model construction of medical students′ suicidal ideation based on machine learning algorithm

ZHANG Yilin; WANG Chao; LI Mengdie; LIU Zhaorui; LYU Juncheng

doi:10.16462/j.cnki.zhjbkz.2023.11.013

Volume 27 Issue 11

Nov. 2023

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ZHANG Yilin, WANG Chao, LI Mengdie, LIU Zhaorui, LYU Juncheng. Influencing factors analysis and prediction model construction of medical students′ suicidal ideation based on machine learning algorithm[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(11): 1320-1328. doi: 10.16462/j.cnki.zhjbkz.2023.11.013

Citation:

ZHANG Yilin, WANG Chao, LI Mengdie, LIU Zhaorui, LYU Juncheng. Influencing factors analysis and prediction model construction of medical students′ suicidal ideation based on machine learning algorithm[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(11): 1320-1328. doi: 10.16462/j.cnki.zhjbkz.2023.11.013

Citation:

ZHANG Yilin, WANG Chao, LI Mengdie, LIU Zhaorui, LYU Juncheng. Influencing factors analysis and prediction model construction of medical students′ suicidal ideation based on machine learning algorithm[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(11): 1320-1328. doi: 10.16462/j.cnki.zhjbkz.2023.11.013

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Influencing factors analysis and prediction model construction of medical students′ suicidal ideation based on machine learning algorithm

doi: 10.16462/j.cnki.zhjbkz.2023.11.013

1.
School of Public Health, Weifang Medical University, Weifang 261053, China
2.
Peking University Sixth Hospital, Peking University Institute of Mental Health, National Health Commission Key Laboratory of Mental Health, National Clinical Research Center for Mental Disorders, Beijing 100083, China

Funds:

Natural Science Foundation of Shandong Province ZR2021MH408

Teaching Case of Professional Degree Postgraduates in Graduate Education of Shandong Province SDYAL20153

Key Research Topic of Academic Degree and Postgraduate Education in China 2020ZDB44

More Information

Corresponding author: LYU Juncheng, E-mail: cheng_China@163.com
Received Date: 2022-09-23
Rev Recd Date: 2023-01-06

Available Online: 2023-11-20

Publish Date: 2023-11-10

Abstract

Abstract

Objective The purpose of this study is to understand the factors influencing suicidal ideation among medical students and to explore the predictive effect of machine learning (ML) algorithms. Methods A random stratified whole-group sampling of medical students in Shandong Province was conducted from November 2021 to March 2022 to conduct the questionnaire survey. The Chi-square test, Fisher′s exact probability method, and Logistic regression were used to explore the factors influencing suicidal ideation among medical students. The prediction models were constructed based on a machine learning algorithm in the training set. The predictive ability of the model was evaluated in the test set based on accuracy, sensitivity, and so on. Results The suicidal ideation rate in medical students was 12.80%. The results showed that living in rural areas (OR=1.523, 95% CI: 1.023-2.271, P=0.039), and depression symptoms (OR=3.874, 95% CI: 2.676-5.621, P < 0.001) were risk factors for suicidal ideation. Having not been in love in the past two years (OR=0.601, 95% CI: 0.427-0.841, P=0.003) and so on were protective factors for suicidal ideation. The prediction accuracy and sensitivity of the four models were higher than 0.90, the Kappa values were higher than 0.80, and the positive and negative predictive values were higher than 0.90. Conclusions All four prediction models perform well, among which the suicide ideation prediction model based on support vector machine (SVM) has more advantages. The SVM model can effectively predict the risk of suicidal ideation, which is more helpful for early identification and intervention of high-risk groups.
- Medical students,
- Machine learning,
- Suicidal ideation,
- Prediction model

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References(28)

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