Yoon Hee Soo, Kim Min Jin, Lim Kang Hyeon, Kim Min Suk, Kang Byung Jae, Rah Yoon Chan, Choi June
Department of Otorhinolaryngology-Head and Neck Surgery, Korea University College of Medicine, Ansan Hospital, Ansan 15355, Republic of Korea.
Department of Biostatistics, Korea University College of Medicine, Seoul 08308, Republic of Korea.
Diagnostics (Basel). 2024 Sep 10;14(18):2000. doi: 10.3390/diagnostics14182000.
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.
这项回顾性横断面研究旨在使用预测建模技术和老年人听力障碍问卷（HHIE）评估慢性中耳炎（COM）患者的功能性听力能力。本研究调查了预测模型识别COM患者听力水平的潜力。
我们全面检查了289名被诊断为COM的个体，其中136人报告有耳鸣，143人没有。本研究对各种患者特征和HHIE问卷结果进行了详细分析。采用逻辑回归模型和随机森林模型，并根据关键性能指标进行比较。
逻辑回归模型在准确率（73.56%）、曲线下面积（AUC；0.73）、Kappa值（0.45）和F1分数（0.78）方面略高于随机森林模型。这些结果表明逻辑回归模型在识别COM患者听力水平方面具有更好的预测性能。
尽管逻辑回归的AUC未达到基准，但本研究强调了使用更大数据集提高可靠性和改进性能指标的潜力。预测建模技术与HHIE问卷的结合显示出有望实现更高的诊断准确性，并完善COM患者的干预策略。
