Sadegh-Zadeh Seyed-Ali, Soleimani Mamalo Alireza, Kavianpour Kaveh, Atashbar Hamed, Heidari Elham, Hajizadeh Reza, Roshani Amir Sam, Habibzadeh Shima, Saadat Shayan, Behmanesh Majid, Saadat Mozafar, Gargari Sahar Sayyadi
Department of Computing, School of Digital, Technologies and Arts, Staffordshire University, Stoke-on-Trent, United Kingdom.
Student Research Committee, Urmia University of Medical Sciences, Urmia, Iran.
Front Artif Intell. 2024 May 7;7:1381455. doi: 10.3389/frai.2024.1381455. eCollection 2024.
This research investigates the application of machine learning to improve the diagnosis of tinnitus using high-frequency audiometry data. A Logistic Regression (LR) model was developed alongside an Artificial Neural Network (ANN) and various baseline classifiers to identify the most effective approach for classifying tinnitus presence. The methodology encompassed data preprocessing, feature extraction focused on point detection, and rigorous model evaluation through performance metrics including accuracy, Area Under the ROC Curve (AUC), precision, recall, and F1 scores. The main findings reveal that the LR model, supported by the ANN, significantly outperformed other machine learning models, achieving an accuracy of 94.06%, an AUC of 97.06%, and high precision and recall scores. These results demonstrate the efficacy of the LR model and ANN in accurately diagnosing tinnitus, surpassing traditional diagnostic methods that rely on subjective assessments. The implications of this research are substantial for clinical audiology, suggesting that machine learning, particularly advanced models like ANNs, can provide a more objective and quantifiable tool for tinnitus diagnosis, especially when utilizing high-frequency audiometry data not typically assessed in standard hearing tests. The study underscores the potential for machine learning to facilitate earlier and more accurate tinnitus detection, which could lead to improved patient outcomes. Future work should aim to expand the dataset diversity, explore a broader range of algorithms, and conduct clinical trials to validate the models' practical utility. The research highlights the transformative potential of machine learning, including the LR model and ANN, in audiology, paving the way for advancements in the diagnosis and treatment of tinnitus.
本研究探讨了利用机器学习通过高频听力测定数据来改善耳鸣诊断的应用。开发了一个逻辑回归（LR）模型以及一个人工神经网络（ANN）和各种基线分类器，以确定用于对耳鸣存在情况进行分类的最有效方法。该方法包括数据预处理、专注于点检测的特征提取，以及通过包括准确率、ROC曲线下面积（AUC）、精确率、召回率和F1分数等性能指标进行严格的模型评估。主要研究结果表明，在人工神经网络的支持下，逻辑回归模型显著优于其他机器学习模型，准确率达到94.06%，AUC为97.06%，并具有较高的精确率和召回率分数。这些结果证明了逻辑回归模型和人工神经网络在准确诊断耳鸣方面的有效性，超越了依赖主观评估的传统诊断方法。这项研究对临床听力学具有重大意义，表明机器学习，特别是像人工神经网络这样的先进模型，可以为耳鸣诊断提供更客观和可量化的工具，尤其是在利用标准听力测试中通常不评估的高频听力测定数据时。该研究强调了机器学习促进更早、更准确耳鸣检测的潜力，这可能会改善患者的治疗效果。未来的工作应旨在扩大数据集的多样性，探索更广泛的算法，并进行临床试验以验证模型的实际效用。该研究突出了机器学习，包括逻辑回归模型和人工神经网络，在听力学中的变革潜力，为耳鸣的诊断和治疗进展铺平了道路。
