TY  - JOUR
PY  - 2022//
TI  - Prediction of suicidal ideation among Chinese college students based on radial basis function neural network
JO  - Frontiers in public health
A1  - Liao, Shiyi
A1  - Wang, Yang
A1  - Zhou, Xiaonan
A1  - Zhao, Qin
A1  - Li, Xiaojing
A1  - Guo, Wanjun
A1  - Ji, Xiaoyi
A1  - Lv, Qiuyue
A1  - Zhang, Yunyang
A1  - Zhang, Yamin
A1  - Deng, Wei
A1  - Chen, Ting
A1  - Li, Tao
A1  - Qiu, Peiyuan
SP  - e1042218
EP  - e1042218
VL  - 10
IS  - 
N2  - BACKGROUND: Suicide is one of the leading causes of death for college students. The predictors of suicidal ideation among college students are inconsistent and few studies have systematically investigated psychological symptoms of college students to predict suicide. Therefore, this study aims to develop a suicidal ideation prediction model and explore important predictors of suicidal ideation among college students in China. <br><br>METHODS: We recruited 1,500 college students of Sichuan University and followed up for 4 years. Demographic information, behavioral and psychological information of the participants were collected using computer-based questionnaires. The Radial Basis Function Neural Network (RBFNN) method was used to develop three suicidal ideation risk prediction models and to identify important predictive factors for suicidal ideation among college students. <br><br>RESULTS: The incidence of suicidal ideation among college students in the last 12 months ranged from 3.00 to 4.07%. The prediction accuracies of all the three models were over 91.7%. The area under curve scores were up to 0.96. Previous suicidal ideation and poor subjective sleep quality were the most robust predictors. Poor self-rated mental health has also been identified to be an important predictor. Paranoid symptom, internet addiction, poor self-rated physical health, poor self-rated overall health, emotional abuse, low average annual household income per person and heavy study pressure were potential predictors for suicidal ideation. <br><br>CONCLUSIONS: The study suggested that the RBFNN method was accurate in predicting suicidal ideation. And students who have ever had previous suicidal ideation and poor sleep quality should be paid consistent attention to.<p />  <p>Language: en</p>
LA  - en
SN  - 2296-2565
UR  - http://dx.doi.org/10.3389/fpubh.2022.1042218
ID  - ref1
ER  -