Enhanced classification and severity prediction of major depressive disorder using acoustic features and machine learning

Bibliographic Data

ID15522504
AuthorsLijuan Liang (0000-0002-3439-8673, Hainan Medical University), Yang Wang (0009-0007-0551-9820, Nanjing Brain Hospital), Hui Ma (0000-0001-9662-0447, Hainan Provincial Hospital of Traditional Chinese Medicine), Ran Zhang (0000-0001-8483-2565, Nanjing Brain Hospital), Rongxun Liu (0009-0005-3147-3593, Nanjing Brain Hospital), Rongxin Zhu (0009-0006-6213-5877, Nanjing Brain Hospital), Zhiguo Zheng (0009-0004-3588-7729, Hainan University), Xizhe Zhang (0000-0002-8684-4591, Nanjing Brain Hospital, corresponding author), Fei Wang (0000-0003-0860-6317, Nanjing Brain Hospital, corresponding author)
Year2024
Volume15
Pages1422020-1422020
Publication date2024-09-17
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Psychiatry (JOURNAL)
Journal identifiersISSN: 1664-0640 • E-ISSN: 1664-0640
PublisherFrontiers Media (PUBLISHER • CH)
DOI10.3389/fpsyt.2024.1422020
PMID39355380
OpenAlexW4402603760
LanguageEN
Citations received2
References cited6

The vocal acoustic features can not only effectively classify the major depression and the healthy control groups, but also accurately predict the severity of depressive symptoms

Anxiety · Audiology · Correlation · Depression (economics · Hamd · Machine learning · Major depressive disorder · Mood · Psychiatry · Receiver operating characteristic · Sample size determination · Statistics · Clinical Psychology · Computer Science · Emotion and Mood Recognition · Mathematics · Medicine · Mental Health Research Topics · Mental Health via Writing · Psychology · Artificial Intelligence

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