In the field of audiology, achieving accurate discrimination of auditory impairments remains a formidable challenge. Conditions such as deafness and tinnitus exert a substantial impact on patients’ overall quality of life, emphasizing the urgent need for precise and efficient classification methods. This study introduces an innovative approach, utilizing Multi-View Brain Network data acquired from three distinct cohorts: 51 deaf patients, 54 with tinnitus, and 42 normal controls. Electroencephalogram (EEG) recording data were meticulously collected, focusing on 70 electrodes attached to an end-to-end key with 10 regions of interest (ROI). This data is synergistically integrated with machine learning algorithms. To tackle the inherently high-dimensional nature of brain connectivity data, principal component analysis (PCA) is employed for feature reduction, enhancing interpretability. The proposed approach undergoes evaluation using ensemble learning techniques, including Random Forest, Extra Trees, Gradient Boosting, and CatBoost. The performance of the proposed models is scrutinized across a comprehensive set of metrics, encompassing cross-validation accuracy (CVA), precision, recall, F1-score, Kappa, and Matthews correlation coefficient (MCC). The proposed models demonstrate statistical significance and effectively diagnose auditory disorders, contributing to early detection and personalized treatment, thereby enhancing patient outcomes and quality of life. Notably, they exhibit reliability and robustness, characterized by high Kappa and MCC values. This research represents a significant advancement in the intersection of audiology, neuroimaging, and machine learning, with transformative implications for clinical practice and care.

中文翻译：

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多视图大脑网络和先进机器学习技术的协同集成用于听觉障碍诊断

在听力学领域，实现听觉障碍的准确辨别仍然是一个艰巨的挑战。耳聋、耳鸣等疾病对患者的整体生活质量产生重大影响，迫切需要精确有效的分类方法。这项研究引入了一种创新方法，利用从三个不同队列获得的多视图大脑网络数据：51 名聋哑患者、54 名耳鸣患者和 42 名正常对照。脑电图 (EEG) 记录数据经过精心收集，重点关注连接到具有 10 个感兴趣区域 (ROI) 的端到端按键的 70 个电极。这些数据与机器学习算法协同集成。为了解决大脑连接数据固有的高维性质，采用主成分分析 (PCA) 来减少特征，增强可解释性。所提出的方法使用集成学习技术进行评估，包括随机森林、额外树、梯度提升和 CatBoost。所提出模型的性能通过一组全面的指标进行审查，包括交叉验证准确性 (CVA)、精度、召回率、F1 分数、Kappa 和 Matthews 相关系数 (MCC)。所提出的模型具有统计学意义，可以有效诊断听觉障碍，有助于早期发现和个性化治疗，从而提高患者的治疗效果和生活质量。值得注意的是，它们表现出可靠性和稳健性，其特点是高 Kappa 和 MCC 值。这项研究代表了听力学、神经影像学和机器学习交叉领域的重大进步，对临床实践和护理具有变革性的影响。