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Machine Learning on Early Diagnosis of Depressionopen access

Authors
Lee, Kwang-SigHam, Byung-Joo
Issue Date
8월-2022
Publisher
KOREAN NEUROPSYCHIATRIC ASSOC
Keywords
Keywords Depression; Early diagnosis; Machine learning
Citation
PSYCHIATRY INVESTIGATION, v.19, no.8, pp.597 - 605
Indexed
SCIE
SSCI
SCOPUS
KCI
Journal Title
PSYCHIATRY INVESTIGATION
Volume
19
Number
8
Start Page
597
End Page
605
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/144108
DOI
10.30773/pi.2022.0075
ISSN
1738-3684
Abstract
To review the recent progress of machine learning for the early diagnosis of depression (major depressive disorder). The source of data was 32 original studies in the Web of Science. The search terms were ???depression??? (title) and ???random forest??? (abstract). The eligibility criteria were the dependent variable of depression, the interventions of machine learning (the decision tree, the na??ve Bayesian, the random forest, the support vector machine and/or the artificial neural network), the outcomes of accuracy and/or the area under the receiver operating characteristic curve (AUC) for the early diagnosis of depression, the publication year of 2000 or later, the publication language of English and the publication journal of SCIE/SSCI. Different machine learning methods would be appropriate for different types of data for the early diagnosis of depression, e.g., logistic regression, the random forest, the support vector machine and/or the artificial neural network in the case of numeric data, the random forest in the case of genomic data. Their performance measures reported varied within 60.1???100.0 for accuracy and 64.0???96.0 for the AUC. Machine learning provides an effective, non-invasive decision support system for early diagnosis of depression. Psychiatry Investig 2022;19(8):597-605
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