Apriori algorithm based prediction of students’ mental health risks in the context of artificial intelligence

This study demonstrates the potential of AI-powered predictive models for early identification of students at risk of mental health challenges. The findings have significant implications for improving mental health services within higher education institutions. Future research should focus on further refining the model, incorporating real-time data streams, and developing personalized intervention strategies based on the model's predictions.

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Publication Details

Journal
Frontiers in Public Health
Published
2025-02-12
DOI
https://doi.org/10.3389/fpubh.2025.1533934
Citations
6
Primary Topic
Mental Health Research Topics
Type
article
Field-Weighted Citation Impact
12.71
Controls
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article

Apriori algorithm based prediction of students’ mental health risks in the context of artificial intelligence

You Fu, Fang Ren, Jiantao Lin
6 citations
Frontiers in Public Health
Mental Health Research Topics
12.71
article

Apriori algorithm based prediction of students’ mental health risks in the context of artificial intelligence

You Fu, Fang Ren, Jiantao Lin
article en
6 citations

Abstract

This study demonstrates the potential of AI-powered predictive models for early identification of students at risk of mental health challenges. The findings have significant implications for improving mental health services within higher education institutions. Future research should focus on further refining the model, incorporating real-time data streams, and developing personalized intervention strategies based on the model's predictions.

Frontiers in Public HealthVol. 13
Tianjin University (CN), Shaanxi Normal University (CN)
Openalex Percentile: Top 2%
Mental Health Research Topics
12.71
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