Madububambachu Ujunwa, Ukpebor Augustine, Ihezue Urenna
School of Computing Sciences and Computer Engineering, University of Southern Mississippi, Hattiesburg, Mississippi, United States of America.
Cyber Insights, Jackson, Mississippi, United States of America.
Clin Pract Epidemiol Ment Health. 2024 Jul 26;20:e17450179315688. doi: 10.2174/0117450179315688240607052117. eCollection 2024.
This study aims to investigate the potential of machine learning in predicting mental health conditions among college students by analyzing existing literature on mental health diagnoses using various machine learning algorithms.
The research employed a systematic literature review methodology to investigate the application of deep learning techniques in predicting mental health diagnoses among students from 2011 to 2024. The search strategy involved key terms, such as "deep learning," "mental health," and related terms, conducted on reputable repositories like IEEE, Xplore, ScienceDirect, SpringerLink, PLOS, and Elsevier. Papers published between January, 2011, and May, 2024, specifically focusing on deep learning models for mental health diagnoses, were considered. The selection process adhered to PRISMA guidelines and resulted in 30 relevant studies.
The study highlights Convolutional Neural Networks (CNN), Random Forest (RF), Support Vector Machine (SVM), Deep Neural Networks, and Extreme Learning Machine (ELM) as prominent models for predicting mental health conditions. Among these, CNN demonstrated exceptional accuracy compared to other models in diagnosing bipolar disorder. However, challenges persist, including the need for more extensive and diverse datasets, consideration of heterogeneity in mental health condition, and inclusion of longitudinal data to capture temporal dynamics.
This study offers valuable insights into the potential and challenges of machine learning in predicting mental health conditions among college students. While deep learning models like CNN show promise, addressing data limitations and incorporating temporal dynamics are crucial for further advancements.
本研究旨在通过分析利用各种机器学习算法进行心理健康诊断的现有文献，探讨机器学习在预测大学生心理健康状况方面的潜力。
该研究采用系统文献综述方法，调查2011年至2024年深度学习技术在预测学生心理健康诊断中的应用。搜索策略涉及在IEEE、Xplore、ScienceDirect、SpringerLink、PLOS和Elsevier等知名数据库中使用“深度学习”“心理健康”等关键词及相关术语进行搜索。考虑2011年1月至2024年5月期间发表的、专门关注心理健康诊断深度学习模型的论文。选择过程遵循PRISMA指南，最终得到30项相关研究。
该研究强调卷积神经网络（CNN）、随机森林（RF）、支持向量机（SVM）、深度神经网络和极限学习机（ELM）是预测心理健康状况的突出模型。其中，与其他模型相比，CNN在诊断双相情感障碍方面表现出卓越的准确性。然而，挑战依然存在，包括需要更广泛和多样的数据集、考虑心理健康状况的异质性以及纳入纵向数据以捕捉时间动态。
本研究为机器学习在预测大学生心理健康状况方面的潜力和挑战提供了有价值的见解。虽然像CNN这样的深度学习模型显示出前景，但解决数据限制并纳入时间动态对于进一步发展至关重要。
