| Title |
Research on Predicting the Mental Health of College Students with Prediction Models based on Big Data Technology |
| Authors |
(Peng Zhang) ; (Wenjing Han) ; (Quanzhi Liu) |
| DOI |
https://doi.org/10.5573/IEIESPC.2024.13.4.393 |
| Keywords |
Big data; College student; Mental health; Predictive model |
| Abstract |
The mental health of college students is facing challenges because of the rapid changes in society. Anticipating these changes to enhance the emotional well-being of college students is crucial. This study devised a questionnaire focusing on pressure sources, such as employment and academic pressures. The mental health of college students was assessed using the SCL-90 scale, and data were collected as samples. A predictive model based on a back-propagation neural network (BPNN) was then constructed. The BPNN parameters were fine-tuned using the improved seagull optimization algorithm (ISOA), resulting in the ISOA-BPNN prediction model. The ISOA algorithm improved the BPNN prediction performance significantly compared to optimization algorithms, such as particle swarm optimization (PSO) and artificial bee colony (ABC), achieving an accuracy of 0.9762, an F1 value of 0.9834, and an area under the curve (AUC) of 0.9956. The ISOA-BPNN model demonstrated superior performance in predicting the mental health status of college students compared to prediction models, such as Logistic regression. These findings confirm the reliability of the ISOA-BPNN model developed in this study for predicting the mental health of college students and its potential applicability. |