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Review
. 2024 Jul 26:20:e17450179315688.
doi: 10.2174/0117450179315688240607052117. eCollection 2024.

Machine Learning Techniques to Predict Mental Health Diagnoses: A Systematic Literature Review

Ujunwa Madububambachu  1 , Augustine Ukpebor  2 , Urenna Ihezue  3
Affiliations
Review

Machine Learning Techniques to Predict Mental Health Diagnoses: A Systematic Literature Review

Ujunwa Madububambachu et al. Clin Pract Epidemiol Ment Health. .

Abstract

Introduction: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Keywords: Algorithm; CNN; Deep learning; Machine learning; Mental health; Prediction.

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Conflict of interest statement

The authors declared no conflict of interest, financial or otherwise.

Figures

Fig. (1)
Fig. (1)
Okoli’s guide for conducting a standalone systematic literature review.
Fig. (2)
Fig. (2)
Taxonomy of the systematic literature review for this study [15].
Fig. (3)
Fig. (3)
Flow diagram of the study selection process.
Fig. (4)
Fig. (4)
Diagram of reviewed papers.
See this image and copyright information in PMC

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