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

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 m...

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Bibliographic Details
Published in:Diagnostics (Basel) 2024-09, Vol.14 (18), p.2000
Main Authors: Yoon, Hee Soo, Kim, Min Jin, Lim, Kang Hyeon, Kim, Min Suk, Kang, Byung Jae, Rah, Yoon Chan, Choi, June
Format: Article
Language:English
Subjects:
Accuracy
Aged
Audiometry
Business performance management
Chronic illnesses
chronic otitis media
Computer industry
Data collection
Diabetes
Ear diseases
Ears & hearing
hearing level
Hearing loss
hearing prediction
Hearing protection
HHIE
Hyperlipidemia
Hypertension
Kidney diseases
Otitis media
Patients
Questionnaires
Regression analysis
Surgery
Surveys
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Summary: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. 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. 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. 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.
ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics14182000