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

Journal: Journal of otolaryngology - head & neck surgery = Le Journal d'oto-rhino-laryngologie et de chirurgie cervico-faciale
PMID: 37730624

Abstract

BACKGROUND: A multidimensional voice quality assessment is recommended for all patients with dysphonia, which requires a patient visit to the otolaryngology clinic. The aim of this study was to determine the accuracy of an online artificial intelligence classifier, the Online Sequential Extreme Learning Machine (OSELM), in detecting voice pathology. In this study, a Malaysian Voice Pathology Database (MVPD), which is the first Malaysian voice database, was created and tested.

Authors

  • Nur Ain Nabila Za'im
    Department of Otorhinolaryngology-Head and Neck Surgery, Faculty of Medicine, Universiti Kebangsaan Malaysia (UKM), Hospital Canselor Tuanku Muhriz, Jalan Yaacob Latif, Bandar Tun Razak, 56000, Kuala Lumpur, Malaysia.
  • Fahad Taha Al-Dhief
    Faculty of Electrical Engineering, Department of Communication Engineering, Universiti Teknologi Malaysia, UTM Johor Bahru, Johor, Malaysia.
  • Mawaddah Azman
    Department of Otorhinolaryngology Head and Neck Surgery, University Kebangsaan Malaysia Medical Center, Kuala Lumpur, Malaysia.
  • Majid Razaq Mohamed Alsemawi
    College of Information Technology, Imam Ja'afar Al-Sadiq University, Al-Muthanna, 66001, Iraq.
  • Nurul Mu Azzah Abdul Latiff
    Faculty of Electrical Engineering, Universiti Teknologi Malaysia (UTM), 81310, Johor Bahru, Johor, Malaysia.
  • Marina Mat Baki
    Department of Otorhinolaryngology-Head and Neck Surgery, Faculty of Medicine, Universiti Kebangsaan Malaysia (UKM), Hospital Canselor Tuanku Muhriz, Jalan Yaacob Latif, Bandar Tun Razak, 56000, Kuala Lumpur, Malaysia. marinamatbaki@ppukm.ukm.edu.my.

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