Hybrid statistical and machine-learning approach to hearing-loss identification based on an oversampling technique.

Journal: Computers in biology and medicine
Published Date: Dec 12, 2024

Abstract

BACKGROUND AND OBJECTIVES: Hearing loss is a crucial global health hazard exerting considerable social and physiological effects on spoken language and cognition. Patients affected by this condition may experience social and professional hardships that dominate occupational injuries. Therefore, the identification of the features of recessive hearing loss is important for clinicians to prevent further disease progression. This work aimed to develop a hybrid statistical and machine-learning approach as a decision-support mechanism. We expect the proposed model to help predict hearing-loss disorders and support clinical diagnosis.

Authors

  • Tang-Chuan Wang
    Department of Otolaryngology-Head and Neck Surgery, China Medical University Hsinchu Hospital, Zhubei City, Hsinchu, 302056, Taiwan, ROC; Department of Master Program for Biomedical Engineering, College of Biomedical Engineering, China Medical University, Taichung, 404328, Taiwan, ROC; School of Medicine, College of Medicine, China Medical University, Taichung, 404328, Taiwan, ROC. Electronic address: tangchuan1020@gmail.com.
  • Ko-Han Sun
    Department of Business Administration, National Chung Hsing University, South District, Taichung, 402202, Taiwan, ROC. Electronic address: r3564123@icloud.com.
  • Mingchang Chih
    Department of Business Administration, National Chung Hsing University, South District, Taichung, 402202, Taiwan, ROC. Electronic address: mcchih@dragon.nchu.edu.tw.
  • Wei-Chun Chen
    Bachelor Program in Industrial Technology, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 64002, Taiwan.

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