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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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ISSN:2055-2076
DOI:10.1177/20552076251411968