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Explainable artificial intelligence approaches for predicting depression by combining feature selection methods and machine learning classifiers.

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Title: Explainable artificial intelligence approaches for predicting depression by combining feature selection methods and machine learning classifiers.
Authors: Kim MG; SKKU Business School, Sungkyunkwan University, Seoul, Republic of Korea., Lee KC; Department of Surgery School of Medicine, Kangbuk Samsung Hospital Sungkyunkwan University, Seoul 03181, Republic of Korea., Lee K; Department of Surgery School of Medicine, Kangbuk Samsung Hospital Sungkyunkwan University, Seoul 03181, Republic of Korea., Kim HU; Department of Surgery School of Medicine, Kangbuk Samsung Hospital Sungkyunkwan University, Seoul 03181, Republic of Korea., Seo YW; Department of Business Consulting, Daejeon University, Daejeon, Republic of Korea., Chae SW; Department of Digital Business, Hoseo University, Cheonan, Republic of Korea.
Source: Digital health [Digit Health] 2026 Jan 20; Vol. 12, pp. 20552076251411968. Date of Electronic Publication: 2026 Jan 20 (Print Publication: 2026).
Publication Type: Journal Article
Journal Info: Publisher: SAGE Publications Ltd Country of Publication: United States NLM ID: 101690863 Publication Model: eCollection Cited Medium: Print ISSN: 2055-2076 (Print) Linking ISSN: 20552076 NLM ISO Abbreviation: Digit Health Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
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  Data: <searchLink fieldCode="AU" term="%22Kim+MG%22">Kim MG</searchLink>; SKKU Business School, Sungkyunkwan University, Seoul, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Lee+KC%22">Lee KC</searchLink>; Department of Surgery School of Medicine, Kangbuk Samsung Hospital Sungkyunkwan University, Seoul 03181, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Lee+K%22">Lee K</searchLink>; Department of Surgery School of Medicine, Kangbuk Samsung Hospital Sungkyunkwan University, Seoul 03181, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Kim+HU%22">Kim HU</searchLink>; Department of Surgery School of Medicine, Kangbuk Samsung Hospital Sungkyunkwan University, Seoul 03181, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Seo+YW%22">Seo YW</searchLink>; Department of Business Consulting, Daejeon University, Daejeon, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Chae+SW%22">Chae SW</searchLink>; Department of Digital Business, Hoseo University, Cheonan, Republic of Korea.
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  Data: <searchLink fieldCode="JN" term="%22101690863%22">Digital health</searchLink> [Digit Health] 2026 Jan 20; Vol. 12, pp. 20552076251411968. <i>Date of Electronic Publication: </i>2026 Jan 20 (<i>Print Publication: </i>2026).
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22SAGE+Publications+Ltd%22">SAGE Publications Ltd </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>101690863 <i>Publication Model: </i>eCollection <i>Cited Medium: </i>Print <i>ISSN: </i>2055-2076 (Print) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2220552076%22">20552076 </searchLink><i>NLM ISO Abbreviation: </i>Digit Health <i>Subsets: </i>PubMed not MEDLINE
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        Value: 10.1177/20552076251411968
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              Text: 2026 Jan 20
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