A NEW ARTIFICIAL INTELLIGENCE-BASED CLINICAL DECISION SUPPORT SYSTEM FOR DIAGNOSIS OF MAJOR PSYCHIATRIC DISEASES BASED ON VOICE ANALYSIS

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dc.contributor.authorCansel, Neslihan
dc.contributor.authorAlcin, Ömer Faruk
dc.contributor.authorYılmaz, Ömer Furkan
dc.contributor.authorAri, Ali
dc.contributor.authorAkan, Mustafa
dc.contributor.authorUcuz, İlknur
dc.date.accessioned2026-06-19T06:32:04Z
dc.date.available2026-06-19T06:32:04Z
dc.date.issued2023
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractBackground: Speech features are essential components of psychiatric examinations, serving as important markers in the recognition and monitoring of mental illnesses. This study aims to develop a new clinical decision support system based on artificial intelligence, utilizing speech signals to distinguish between bipolar, depressive, anxiety and schizophrenia spectrum disorders. Subjects and methods: A total of 79 patients, who were admitted to the psychiatry clinic between 2020-2021, including 15 with schizophrenia spectrum disorders, 24 with anxiety disorders, 25 with depressive disorders, and 15 with bipolar affective disorder, alongside with 25 healthy individuals were included in the study. The speech signal dataset was created by recording participants’ readings of two texts determined by the Russell emotion model. The number of speech samples was increased by using random sampling in speech signals. The sample audio signals were decomposed into time-frequency coefficients using Wavelet Packet Transform (WPT). Feature extraction was performed using each coefficient obtained from both Mel-Frequency Cepstral Coefficients (MFCC) and Gammatone Cepstral Coefficient (GTCC) methods. The disorder classification was carried out using k-Nearest Neighbor (kNN) and Support Vector Machine (SVM) classifiers. Results: The success rate of the developed model in distinguishing the disorders was 96.943%. While the kNN model exhibited the highest performance in diagnosing bipolar disorder, it performed the least effectively in detecting depressive disorders. Whereas, the SVM model demonstrated close and high performance in detecting anxiety and psychosis, but its performance was low in identifying bipolar disorder. The findings support the utilization of speech analysis for distinguishing major psychiatric disorders. In this regard, the future development of artificial intelligence-based systems has the potential to enhance the psychiatric diagnosis process. © Medicinska naklada – Zagreb, Croatia.
dc.identifier.doi10.24869/PSYD.2023.489
dc.identifier.endpage499
dc.identifier.issn0353-5053
dc.identifier.issue4
dc.identifier.pmid37992093
dc.identifier.scopus2-s2.0-85177853488
dc.identifier.scopusqualityQ3
dc.identifier.startpage489
dc.identifier.urihttps://doi.org/10.24869/PSYD.2023.489
dc.identifier.urihttps://hdl.handle.net/20.500.12899/4937
dc.identifier.volume35
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMedicinska Naklada Zagreb
dc.relation.ispartofPsychiatria Danubina
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_Scopus_20260612
dc.subjectArtificial Intelligence
dc.subjectMental Illness
dc.subjectPsychiatry
dc.subjectRussel Emotion Model
dc.subjectSpeech Signal
dc.titleA NEW ARTIFICIAL INTELLIGENCE-BASED CLINICAL DECISION SUPPORT SYSTEM FOR DIAGNOSIS OF MAJOR PSYCHIATRIC DISEASES BASED ON VOICE ANALYSIS
dc.typeArticle

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