Psychiatry Investig.  2019 Apr;16(4):262-269. 10.30773/pi.2018.12.21.2.

Review of Machine Learning Algorithms for Diagnosing Mental Illness

• Cho G
• Yim J
• Choi Y
• Ko J
• Lee SH

• KMID: 2447783
• DOI: http://doi.org/10.30773/pi.2018.12.21.2

Abstract

OBJECTIVE
Enhanced 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.
METHODS
Researches 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.
RESULTS
Based 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.
CONCLUSION
Researchers 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.

Keyword

Machine learning; Mental illness; Big data

MeSH Terms

Bays
Forests
Health Care Sector
Internet
Learning
Machine Learning*
Mental Health
Residence Characteristics
Sample Size
Support Vector Machine