The objectives of this research are analysing the performance of the state-of-the-art machine learning techniques for classifying COVID-19 from cough sound and identifying the model(s) that consistently perform well across different cough datasets. Different performance evaluation metrics (such as precision, sensitivity, specificity, AUC, accuracy, etc.) make it difficult to select the best performance model. To address this issue, in this paper, we propose an ensemble-based multi-criteria decision making (MCDM) method for selecting top performance machine learning technique(s) for COVID-19 cough classification. We use four cough datasets, namely Cambridge, Coswara, Virufy, and NoCoCoDa to verify the proposed method. At first, our proposed method uses the audio features of cough samples and then applies machine learning (ML) techniques to classify them as COVID-19 or non-COVID-19. Then, we consider a multi-criteria decision-making (MCDM) method that combines ensemble technologies (i.e., soft and hard) to select the best model. In MCDM, we use the technique for order preference by similarity to ideal solution (TOPSIS) for ranking purposes, while entropy is applied to calculate evaluation criteria weights. In addition, we apply the feature reduction process through recursive feature elimination with cross-validation under different estimators. The results of our empirical evaluations show that the proposed method outperforms the state-of-the-art models.
翻译:这项研究的目标是分析将COVID-19从咳嗽声音中分类的最先进的COVID-19机器学习技术的性能,并查明在不同咳嗽数据集中始终表现良好的模型。不同的性能评估指标(如精确性、敏感性、特殊性、AUC、准确性等)使得难以选择最佳性能模型。为了解决这一问题,我们在本文件中建议采用基于共同的多标准决策方法(MCDM)来选择用于COVID-19咳嗽分类的高级性能机器学习技术(MCDM)。我们使用四个咳嗽数据集,即剑桥、科斯瓦拉、维鲁菲和诺科科达等,来核实拟议的方法。首先,我们提议的方法使用咳嗽样品的音频特征,然后运用机器学习技术将其归类为COVID-19或非COVID-19。然后,我们考虑一种多标准的决策模型(MCDM)方法,将共同性能技术(即软硬和硬性)结合选择最佳模型。在MCDMI中,我们使用模型比力模型,我们采用类似性的方法来降低比重。我们使用的计算方法。在模型中,我们采用模型的计算方法在比重模型中,我们采用类似的比重方法,在比重模型中,我们采用比重模型的计算方法,在比重特性中,我们采用不同的计算方法,在比重方法在比重上显示比重方法用于比重。