Evaluating Prediction Models with Hearing Handicap Inventory for the Elderly in Chronic Otitis Media Patients

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Abstract
Background: This retrospective, cross-sectional study aimed to assess the functional hearing capacity of individuals with Chronic Otitis Media (COM) using prediction modeling techniques and the Hearing Handicap Inventory for the Elderly (HHIE) questionnaire. This study investigated the potential of predictive models to identify hearing levels in patients with COM. Methods: We comprehensively examined 289 individuals diagnosed with COM, of whom 136 reported tinnitus and 143 did not. This study involved a detailed analysis of various patient characteristics and HHIE questionnaire results. Logistic and Random Forest models were employed and compared based on key performance metrics. Results: The logistic model demonstrated a slightly higher accuracy (73.56%), area under the curve (AUC; 0.73), Kappa value (0.45), and F1 score (0.78) than the Random Forest model. These findings suggest the superior predictive performance of the logistic model in identifying hearing levels in patients with COM. Conclusions: Although the AUC for the logistic regression did not meet the benchmark, this study highlights the potential for enhanced reliability and improved performance metrics using a larger dataset. The integration of prediction modeling techniques and the HHIE questionnaire shows promise for achieving greater diagnostic accuracy and refining intervention strategies for individuals with COM.
Funding Information
  • the Korea Medical Device Development Fund funded by the Korean government (Ministry of Science and ICT, Industry and Energy, Health & Welfare, and Food and Drug Safety) (RS-2024-00255350, IITP-2024-RS-2022-00156439)
  • the Ansan-Si hidden champion fostering and supporting project funded by Ansan City (RS-2024-00255350, IITP-2024-RS-2022-00156439)
  • a Grant from the Medical data-driven hospital support project through the Korea Health Information Service (KHIS), funded by the Ministry of Health and Welfare, Republic of Korea (RS-2024-00255350, IITP-2024-RS-2022-00156439)
  • the MSIT (Ministry of Science and ICT), Korea, under the ICAN (ICT Challenge and Advanced Network of HRD) program (RS-2024-00255350, IITP-2024-RS-2022-00156439)

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