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Review of Machine Learning Algorithms for Diagnosing Mental Illness
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논문 기본 정보

Type
Academic journal
Author
Journal
Korean Neuropsychiatric Association PSYCHIATRY INVESTIGATION PSYCHIATRY INVESTIGATION 제16권 제4호 KCI Accredited Journals
Published
2019.1
Pages
262 - 269 (8page)

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Review of Machine Learning Algorithms for Diagnosing Mental Illness
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Abstract· Keywords

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ObjectiveaaEnhanced 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. MethodsaaResearches 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. ResultsaaBased 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. ConclusionaaResearchers 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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References (43)

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Ozgur C / 2015 / Selection of Statistical Software for Solving Big Data Problems: A Guide for Businesses, Students, and Universities / SAGE Open :1~12 google schola OECD What is PISA? google schola McAfee A / 2012 / Big data : the management revolution / Harv Bus Rev 90:60~66 google schola Sagiroglu S / 2013 / Big Data: A Review / IEEE Int Conf Collab Technol Syst (CTS) :42~47 google schola Kruse CS / 2016 / Challenges and opportunities of big data in health care : a systematic review / JMIR Med Inform 4:e38 google schola

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