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.

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