Innovative Machine Learning Models for Student Mental Health Analysis

Abstract

Abstract Background　This paper explores the application of machine learning (ML) techniques to analyze and predict students' mental health issues, providing a comprehensive review of current research in this domain. By examining various ML algorithms such as logistic regression, decision trees, support vector machines, and neural networks, we identify student behaviors and academic performance patterns that can predict mental health problems. The primary focus is to discuss the advantages and limitations of existing research and propose solutions to overcome identified challenges. Our review reveals that ML can significantly enhance early intervention strategies and personalize mental health care plans for students. Specific findings include high accuracy rates achieved by support vector machines and neural networks, the importance of data quality and preprocessing, and the critical need for addressing privacy concerns and algorithmic fairness. Contributions of this paper include a detailed comparison of ML techniques, identification of key challenges in current methodologies, and actionable recommendations for improving data privacy, model interpretability, and the generalizability of results. Our findings underscore the potential of ML to transform mental health support in educational settings by enabling proactive and personalized interventions.