论文标题
咳嗽声音的归一化方法的COVID-19的预测性能比较
Comparison of COVID-19 Prediction Performances of Normalization Methods on Cough Acoustics Sounds
论文作者
论文摘要
该疾病称为新冠状病毒（Covid19）是一种新的病毒呼吸道疾病，于2020年1月13日在中国武汉首次出现。这种疾病的某些症状是发烧，咳嗽，呼吸急促和呼吸困难。在更严重的情况下，死亡可能是由于感染而导致的。 Covid19成为了一段大流行，在一段时间内影响了整个世界。反对流行病的最重要问题是19（+）患者的早期诊断和随访。因此，除了RT-PCR测试外，还可以在识别COVID 19（+）患者时使用医学成像方法。在这项研究中，提出了一种使用咳嗽数据，这是Covid19（+）患者最突出的症状之一。在这些数据上研究了Z级别化和最低最大标准化方法的性能。使用离散小波变换方法获得了所有功能。支持向量机（SVM）用作分类器算法。使用Min-Max归一化分别获得了最高的准确性和F1分数的表现为100％和100％。另一方面，使用Z差异化，获得的最高精度和最高的F1得分性能分别为99.2％和99.0％。鉴于结果，很明显，咳嗽声数据将对COVID19病例产生重大贡献。
The disease called the new coronavirus (COVID19) is a new viral respiratory disease that first appeared on January 13, 2020 in Wuhan, China. Some of the symptoms of this disease are fever, cough, shortness of breath and difficulty in breathing. In more serious cases, death may occur as a result of infection. COVID19 emerged as a pandemic that affected the whole world in a little while. The most important issue in the fight against the epidemic is the early diagnosis and follow-up of COVID19 (+) patients. Therefore, in addition to the RT-PCR test, medical imaging methods are also used when identifying COVID 19 (+) patients. In this study, an alternative approach was proposed using cough data, one of the most prominent symptoms of COVID19 (+) patients. The performances of z-normalization and min-max normalization methods were investigated on these data. All features were obtained using discrete wavelet transform method. Support vector machines (SVM) was used as classifier algorithm. The highest performances of accuracy and F1-score were obtained as 100% and 100% using the min-max normalization, respectively. On the other hand, the highest accuracy and highest F1-score performances were obtained as 99.2 % and 99.0 % using the z-normalization, respectively. In light of the results, it is clear that cough acoustic data will contribute significantly to controlling COVID19 cases.
