Objective of this study is to introduce a secure IoHT system, which acts as a clinical decision support system with the diagnosis of cardiovascular diseases. In this sense, it was emphasized that the accuracy rate of diagnosis (classification) can be improved via deep learning algorithms, by needing no hybrid-complex models, and a secure data processing can be achieved with a multi-authentication and Tangle based approach. In detail, heart sounds were classified with Autoencoder Neural Networks (AEN) and the IoHT system was built for supporting doctors in real-time. For developing the diagnosis infrastructure by the AEN, PASCAL B-Training and Physiobank-PhysioNet A-Training heart sound datasets were used accordingly. For the PASCAL dataset, the AEN provided a diagnosis-classification performance with the accuracy of 100%, sensitivity of 100%, and the specificity of 100% whereas the rates were respectively 99.8%, 99.65%, and 99.13% for the PhysioNet dataset. It was seen that the findings by the developed AEN based solution were better than the alternative solutions from the literature. Additionally, usability of the whole IoHT system was found positive by the doctors, and according to the 479 real-case applications, the system was able to achieve accuracy rates of 96.03% for normal heart sounds, 91.91% for extrasystole, and 90.11% for murmur. In terms of security approach, the system was also robust against several attacking methods including synthetic data impute as well as trying to penetrating to the system via central system or mobile devices.

这项研究的目的是介绍一种安全的IoHT系统，该系统可作为诊断心血管疾病的临床决策支持系统。从这个意义上讲，要强调的是，不需要深度复杂的模型，可以通过深度学习算法提高诊断（分类）的准确率，并且可以使用基于多重身份验证和缠结的方法来实现安全的数据处理。详细地，心音通过自动编码器神经网络（AEN）进行分类，并且构建了IoHT系统以实时支持医生。为了通过AEN开发诊断基础结构，相应地使用了PASCAL B训练和Physiobank-PhysioNet A训练心音数据集。对于PASCAL数据集，AEN提供了诊断分类性能，其准确度为100％，灵敏度为100％，的特异性为100％，而PhysioNet数据集的特异性分别为99.8％，99.65％和99.13％。可以看出，已开发的基于AEN的解决方案的发现要优于文献中的替代解决方案。此外，医生发现整个IoHT系统的可用性是肯定的，并且根据479个实际案例的应用，该系统对于正常心音能够达到96.03％的准确率，在心脏收缩期达到91.91％的准确率，对于心律正常的90.11％杂音。在安全性方法方面，该系统还可以抵抗多种攻击方法，包括合成数据估算以及尝试通过中央系统或移动设备渗透到系统。可以看出，已开发的基于AEN的解决方案的发现要优于文献中的替代解决方案。此外，医生发现整个IoHT系统的可用性是肯定的，并且根据479个实际案例的应用，该系统对于正常心音能够达到96.03％的准确率，在心脏收缩期达到91.91％的准确率，对于心律正常的90.11％杂音。在安全性方法方面，该系统还可以抵抗多种攻击方法，包括合成数据估算以及试图通过中央系统或移动设备渗透到系统。可以看出，已开发的基于AEN的解决方案的发现要优于文献中的替代解决方案。此外，医生发现整个IoHT系统的可用性是肯定的，并且根据479个实际案例的应用，该系统能够实现正常心音的准确率为96.03％，心脏收缩期的准确率为91.91％，心脏收缩期的准确率为90.11％。杂音。在安全性方法方面，该系统还可以抵抗多种攻击方法，包括合成数据估算以及尝试通过中央系统或移动设备渗透到系统。该系统对于正常的心音能够达到96.03％的准确率，对于收缩压来说达到91.91％，对于杂音则达到90.11％。在安全性方法方面，该系统还可以抵抗多种攻击方法，包括合成数据估算以及试图通过中央系统或移动设备渗透到系统。该系统对于正常的心音能够达到96.03％的准确率，对于收缩压来说达到91.91％，对于杂音则达到90.11％。在安全性方法方面，该系统还可以抵抗多种攻击方法，包括合成数据估算以及试图通过中央系统或移动设备渗透到系统。