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

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dc.authorscopusid12801110400
dc.authorscopusid56180663900
dc.authorscopusid58733124500
dc.authorscopusid56463872400
dc.authorscopusid57204843862
dc.authorscopusid57063226300
dc.contributor.authorCansel N.
dc.contributor.authorAlcin Ö.F.
dc.contributor.authorYılmaz Ö.F.
dc.contributor.authorAri A.
dc.contributor.authorAkan M.
dc.contributor.authorUcuz İ.
dc.date.accessioned2024-08-04T19:59:23Z
dc.date.available2024-08-04T19:59:23Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
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 coe?cients using Wavelet Packet Transform (WPT). Feature extraction was performed using each coe?cient obtained from both Mel-Frequency Cepstral Coe?cients (MFCC) and Gammatone Cepstral Coe?cient (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.en_US
dc.identifier.doi10.24869/PSYD.2023.489
dc.identifier.endpage499en_US
dc.identifier.issn0353-5053
dc.identifier.issue4en_US
dc.identifier.pmid37992093en_US
dc.identifier.scopus2-s2.0-85177853488en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage489en_US
dc.identifier.urihttps://doi.org/10.24869/PSYD.2023.489
dc.identifier.urihttps://hdl.handle.net/11616/90601
dc.identifier.volume35en_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherMedicinska Naklada Zagreben_US
dc.relation.ispartofPsychiatria Danubinaen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial intelligenceen_US
dc.subjectmental illnessen_US
dc.subjectpsychiatryen_US
dc.subjectRussel emotion modelen_US
dc.subjectspeech signalen_US
dc.titleA NEW ARTIFICIAL INTELLIGENCE-BASED CLINICAL DECISION SUPPORT SYSTEM FOR DIAGNOSIS OF MAJOR PSYCHIATRIC DISEASES BASED ON VOICE ANALYSISen_US
dc.typeArticleen_US

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