ArticlePsychiatry investigation2019
Review of Machine Learning Algorithms for Diagnosing Mental Illness.
Article in Psychiatry investigation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 3 of them syntheses that pooled it.
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Who cites it
52 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Applying AI in the Context of the Association Between Device-Based Assessment of Physical Activity and Mental Health: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- Prediction and diagnosis of depression using machine learning with electronic health records data: a systematic review.BMC medical informatics and decision making · 2023Pooled it
- Methodologies for Monitoring Mental Health on Twitter: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Using Machine Learning to Predict Depression among Adolescents Living with HIV in Uganda.Global social welfare : research, policy & practice · 2026Article
- Decoding the genomic symphony: unravelling brain disorders through data integration and machine learning.Molecular psychiatry · 2025Review
- Exploring Informative Items for Bipolar Disorder Classification Using Machine Learning With Anger Coping Styles in Combination With the Mood Disorder Questionnaire and Bipolar Spectrum Diagnostic Scale.Psychiatry investigation · 2025Article
- Using the excitation/inhibition ratio to optimize the classification of autism and schizophrenia.Translational psychiatry · 2025Article
- Revealing sleep and pain reciprocity with wearables and machine learning.Communications medicine · 2025Review
- Investigating Protective and Risk Factors and Predictive Insights for Aboriginal Perinatal Mental Health: Explainable Artificial Intelligence Approach.Journal of medical Internet research · 2025Article
- Advances and Challenges in Depression Marker Research.Neuropsychiatric disease and treatment · 2025Review
- Explainable AI-driven depression detection from social media using natural language processing and black box machine learning models.Frontiers in artificial intelligence · 2025Article
- Virtual reality-assisted prediction of adult ADHD based on eye tracking, EEG, actigraphy and behavioral indices: a machine learning analysis of independent training and test samples.Translational psychiatry · 2024Article
- Multimodal machine learning for language and speech markers identification in mental health.BMC medical informatics and decision making · 2024Article
- Machine learning applications in studying mental health among immigrants and racial and ethnic minorities: an exploratory scoping review.BMC medical informatics and decision making · 2024Article
- Predicting the Climate Impact of Healthcare Facilities Using Gradient Boosting Machines.Cleaner environmental systems · 2024Article
- A primer on the use of machine learning to distil knowledge from data in biological psychiatry.Molecular psychiatry · 2024Review
- Machine Learning Techniques to Predict Mental Health Diagnoses: A Systematic Literature Review.Clinical practice and epidemiology in mental health : CP & EMH · 2024Review
- Predicting place of delivery choice among childbearing women in East Africa: a comparative analysis of advanced machine learning techniques.Frontiers in public health · 2024Article
- A pilot study on AI-driven approaches for classification of mental health disorders.Frontiers in human neuroscience · 2024Article
- Machine learning prediction of anxiety symptoms in social anxiety disorder: utilizing multimodal data from virtual reality sessions.Frontiers in psychiatry · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
objectiveEnhanced technology in computer and internet has driven scale and quality of data to be improved in various areas including healthcare sectors. Machine Learning (ML) has played a pivotal role in efficiently analyzing those big data, but a general misunderstanding of ML algorithms still exists in applying them (e.g., ML techniques can settle a problem of small sample size, or deep learning is the ML algorithm). This paper reviewed the research of diagnosing mental illness using ML algorithm and suggests how ML techniques can be employed and worked in practice.
methodsResearches about mental illness diagnostic using ML techniques were carefully reviewed. Five traditional ML algorithms-Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN)-frequently used for mental health area researches were systematically organized and summarized.
resultsBased on literature review, it turned out that Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN) were frequently employed in mental health area, but many researchers did not clarify the reason for using their ML algorithm though every ML algorithm has its own advantages. In addition, there were several studies to apply ML algorithms without fully understanding the data characteristics.
conclusionResearchers using ML algorithms should be aware of the properties of their ML algorithms and the limitation of the results they obtained under restricted data conditions. This paper provides useful information of the properties and limitation of each ML algorithm in the practice of mental health.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.