A Mental Health Management and Cognitive Behavior Analysis Model of College Students Using Multi-View Clustering...

Area of Science:

• Public Health
• Psychology
• Data Science

Background:

• College students' mental health (CSMH) is crucial for future societal development.
• Current CSMH management strategies are often untargeted and lack personalization.
• Accurate prediction of individual mental health status is essential for effective intervention.

Purpose of the Study:

• To develop an improved method for analyzing and predicting college students' mental health.
• To address the limitations of existing, non-personalized CSMH management strategies.
• To enhance the accuracy of mental health status assessment through multi-perspective data analysis.

Main Methods:

• Utilized a multi-view K-means (MvK-means) algorithm for CSMH data analysis.
• Implemented a multi-view strategy incorporating a weighting mechanism for different data perspectives.
• Assigned varying weight values to data from each view to optimize model evaluation.

Main Results:

• The proposed MvK-means model demonstrated a beneficial impact on analyzing CSMH data.
• The weighting strategy improved the overall evaluation effect of the predictive model.
• The multi-view approach provided a more nuanced understanding of students' mental health indicators.

Conclusions:

• The MvK-means algorithm offers a promising approach for targeted CSMH management.
• Personalized mental health interventions can be facilitated by accurate predictive modeling.
• Further research into multi-perspective data analysis can significantly advance public health initiatives for students.