Demographic and Symptomatic Features of Voice Disorders and Their Potential Application in Classification Using Machine Learning Algorithms.
Background: Studies have used questionnaires of dysphonic symptoms to screen voice disorders. This study investigated whether the differential presentation of demographic and symptomatic features can be applied to computerized classification. Methods: We recruited 100 patients with glottic neoplasm,...
| Publicado en: | Folia Phoniatrica et Logopaedica Vol. 70; no. 3/4; pp. 174 - 183 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Karger AG
2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=132479203&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 132479203 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10217762 GF7 jtl: Folia Phoniatrica et Logopaedica issn: 10217762 maglogo: N pubinfo: dt: 2018 vid: 70 iid: 3/4 pid: 2485 pub: Karger AG artinfo: ui: 132479203 132479203 146768562 132479203 10.1159/000492327 132479203 ppf: 174 ppct: 9 formats: tig: atl: Demographic and Symptomatic Features of Voice Disorders and Their Potential Application in Classification Using Machine Learning Algorithms. aug: au: Tsui, Sheng-Yang Tsao, Yu Lin, Chii-Wann Fang, Shih-Hau Lin, Feng-Chuan Wang, Chi-Te affil: Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan sug: subj: Machine Learning Algorithms Methods Voice Disorders Symptoms Voice Disorders Classification Human Glottis Pathology Laryngeal Neoplasms Diagnosis Phonotrauma Diagnosis Vocal Cord Paralysis Diagnosis Neural Networks (Computer) Validity Decision Trees Sex Factors Age Factors Smoking Age of Onset Scales ab: Background: Studies have used questionnaires of dysphonic symptoms to screen voice disorders. This study investigated whether the differential presentation of demographic and symptomatic features can be applied to computerized classification. Methods: We recruited 100 patients with glottic neoplasm, 508 with phonotraumatic lesions, and 153 with unilateral vocal palsy. Statistical analyses revealed significantly different distributions of demographic and symptomatic variables. Machine learning algorithms, including decision tree, linear discriminant analysis, K-nearest neighbors, support vector machine, and artificial neural network, were applied to classify voice disorders. Results: The results showed that demographic features were more effective for detecting neoplastic and phonotraumatic lesions, whereas symptoms were useful for detecting vocal palsy. When combining demographic and symptomatic variables, the artificial neural network achieved the highest accuracy of 83 ± 1.58%, whereas the accuracy achieved by other algorithms ranged from 74 to 82.6%. Decision tree analyses revealed that sex, age, smoking status, sudden onset of dysphonia, and 10-item voice handicap index scores were significant characteristics for classification. Conclusion: This study demonstrated a significant difference in demographic and symptomatic features between glottic neoplasm, phonotraumatic lesions, and vocal palsy. These features may facilitate automatic classification of voice disorders through machine learning algorithms. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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