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Prediction method for college student's mental health state based on association rules

Published: 31 May 2023 Publication History

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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BIC '23: Proceedings of the 2023 3rd International Conference on Bioinformatics and Intelligent Computing
February 2023
398 pages
ISBN:9798400700200
DOI:10.1145/3592686
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 May 2023

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