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Review
. 2024 Jun 8;10(12):e32548.
doi: 10.1016/j.heliyon.2024.e32548. eCollection 2024 Jun 30.

Diagnosis of mental disorders using machine learning: Literature review and bibliometric mapping from 2012 to 2023

Chandra Mani Sharma  1   2 , Vijayaraghavan M Chariar  1
Affiliations
Review

Diagnosis of mental disorders using machine learning: Literature review and bibliometric mapping from 2012 to 2023

Chandra Mani Sharma et al. Heliyon. .

Abstract

Background: Mental disorders (MDs) are becoming a leading burden in non-communicable diseases (NCDs). As per the World Health Organization's 2022 assessment report, there was a steep increase of 25 % in MDs during the COVID-19 pandemic. Early diagnosis of MDs can significantly improve treatment outcome and save disability-adjusted life years (DALYs). In recent times, the application of machine learning (ML) and deep learning (DL)) has shown promising results in the diagnosis of MDs, and the field has witnessed a huge research output in the form of research publications. Therefore, a bibliometric mapping along with a review of recent advancements is required.

Methods: This study presents a bibliometric analysis and review of the research, published over the last 10 years. Literature searches were conducted in the Scopus database for the period from January 1, 2012, to June 9, 2023. The data was filtered and screened to include only relevant and reliable publications. A total of 2811 journal articles were found. The data was exported to a comma-separated value (CSV) format for further analysis. Furthermore, a review of 40 selected studies was performed.

Results: The popularity of ML techniques in diagnosing MDs has been growing, with an annual research growth rate of 17.05 %. The Journal of Affective Disorders published the most documents (n = 97), while Wang Y. (n = 64) has published the most articles. Lotka's law is observed, with a minority of authors contributing the majority of publications. The top affiliating institutes are the West China Hospital of Sichuan University followed by the University of California, with China and the US dominating the top 10 institutes. While China has more publications, papers affiliated with the US receive more citations. Depression and schizophrenia are the primary focuses of ML and deep learning (DL) in mental disease detection. Co-occurrence network analysis reveals that ML is associated with depression, schizophrenia, autism, anxiety, ADHD, obsessive-compulsive disorder, and PTSD. Popular algorithms include support vector machine (SVM) classifier, decision tree classifier, and random forest classifier. Furthermore, DL is linked to neuroimaging techniques such as MRI, fMRI, and EEG, as well as bipolar disorder. Current research trends encompass DL, LSTM, generalized anxiety disorder, feature fusion, and convolutional neural networks.

Keywords: Bibliometric analysis; Classification; Disease diagnosis; Machine learning; Mental disorders.

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Conflict of interest statement

We declare that there is no conflict of interest. All authors have read the manuscript and agree on its publication in the journal.

Figures

Fig. 1
Fig. 1
Evolution of ML/DL applications in mental disorder diagnosis.
Fig. 2
Fig. 2
Systematic process for bibliometric analysis and review.
Fig. 3
Fig. 3
Annual scientific production of publications.
Fig. 4
Fig. 4
Author productivity through Lotka's law.
Fig. 5
Fig. 5
Authors' collaboration map (type = star, cluster algorithm = walktrap).
Fig. 6
Fig. 6
Most productive affiliations.
Fig. 7
Fig. 7
Corresponding author's countries.
Fig. 8
Fig. 8
Reference publication year spectroscopy.
Fig. 9
Fig. 9
Word tree representation of the top 50 keyword occurrences.
Fig. 10
Fig. 10
Word co-occurrence network.
Fig. 11
Fig. 11
Trend topics in the literature.
Fig. 12
Fig. 12
Thematic mapping under four categories.
Fig. 13
Fig. 13
Thematic evolution of concepts in the field.
Fig. 14
Fig. 14
Pie distribution of different ML methods used in MD diagnostics.
See this image and copyright information in PMC

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