The accuracy of an Online Sequential Extreme Learning Machine in detecting voice pathology using the Malaysian Voice Pathology Database.

Nur Ain Nabila Za'imFahad Taha Al-DhiefMawaddah AzmanMajid Razaq Mohamed AlsemawiNurul Mu Azzah Abdul LatiffMarina Mat Baki

Published in: Journal of otolaryngology - head & neck surgery = Le Journal d'oto-rhino-laryngologie et de chirurgie cervico-faciale (2023)

The OSELM algorithm exhibited the highest accuracy and sensitivity compared to other classifiers in detecting voice pathology, classifying between malignant and benign lesions, and differentiating between structural and non-structural vocal pathology. Hence, it is a promising artificial intelligence that supports an online application to be used as a screening tool to encourage people to seek medical consultation early for a definitive diagnosis of voice pathology.

Keyphrases

artificial intelligence
deep learning
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squamous cell carcinoma
magnetic resonance imaging
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climate change
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radiation therapy
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