Zhengli Wang
ABSTRACT
Student's mental health problem has been more prevalent in recent years. The common method for predicting mental health involves using machine learning algorithms to dig student's psychological traits based on their network behavior. However, there is a risk that data about network behavior could be exposed. In this paper, we propose a prediction method for college student's mental health based on association rules, whereby, following the privacy calculation of psychological assessment data, the internal and external factors affecting student's mental health are digged with the aid of the improved FP-growth algorithm, and the prediction model of mental health status is constructed. The experimental results show that under the premise of satisfying the privacy protection, the model constructed by the improved association rules algorithm can predict the student's mental health state more accurate.
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