Prediction of college students' mental health based on status data

Yang Zhen

doi:10.1117/12.2681175

23 May 2023 Prediction of college students' mental health based on status data

Yang Zhen

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 126451L (2023) https://doi.org/10.1117/12.2681175
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

Abstract

In order to solve the problem of identifying mental health status among college students, firstly we extracted features from college students' status data, including physiological indicators, family income, academic performance and behavior data, then applied the Apriori algorithm improved with pre-pruning strategy to analyze the relationship between each feature and students' mental health, and finally used the XGBoost model optimized by SMOTE+ENN method to predict the mental health based on the results of the feature analysis. The model achieved good predictive performance: precision is 0.87, recall is 0.86, F1 score is 0.86 and AUC is 0.89. The experimental results on the student status data showed that the proposed model outperforms traditional machine learning and deep learning models, thus making our model able to predict the mental health of college students more effectively and accurately.

Citation Download Citation

Yang Zhen "Prediction of college students' mental health based on status data", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 126451L (23 May 2023); https://doi.org/10.1117/12.2681175

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