Mental Health Monitoring and Abnormality Analysis for Sports Majors Based on Dynamic Threshold

Rezumat

The mental health monitoring and abnormality analysis lay the basis for effective stress regulation of sports majors and help them resist mental illness and improve learning efficiency. This paper conducts mental health monitoring and abnormality analysis for sports majors based on a dynamic threshold. Firstly, the authors explained the flow of the dynamic mental health monitoring for sports majors and provided the prediction flow of the proposed model. Then, based on the Gradient Boosting Decision Tree (GBDT) model, the mental health level of primary sports college students was predicted. The research steps were detailed, including data preparation and preprocessing, model training, and prediction error calculation, and the dynamic threshold method with the sliding window was described. The non-parametric dynamic threshold setting was adopted to detect the local abnormality in the evaluation data series of the mental health level for sports majors before introducing the structure and principle of the proposed prediction model. The experimental results validate our strategy for mental health monitoring and abnormality analysis of sports majors. The results of relevant research have essential theoretical and practical significance for the works of the mental health of primary sports students.