Abstract：
BACKGROUND: Despite technological advancement in the field of healthcare, the worldwide burden of illness caused by cardio-vascular diseases (CVDs) is rising, owing mostly to a sharp increase in developing nations that are undergoing fast health transitions. People have been experimenting with techniques to extend their lives since ancient times. Despite this, technology is still a long way from attaining the aim of lowering mortality rates.
METHODS: From methodological perspective, a design Science Research (DSR) approach is adopted in this research. As such, to investigate the current healthcare and interaction systems created for predicting cardiac disease for patients, we first analyzed the body of existing literature. After that, a conceptual framework of the system was designed using the gathered requirements. Based on the conceptual framework, the development of different components of the system was completed. Finally, the evaluation study procedure was developed taking into account the effectiveness, usability and efficiency of the developed system.
RESULTS: To attain the objectives, we proposed a system consisting of a wearable device and mobile application, which allows the users to know their risk levels of having CVDs in the future. The Internet of Things (IoT) and Machine Learning (ML) techniques were adopted to develop the system that can classify its users into three risk levels (high, moderate and low risk of having CVD) with an F1 score of 80.4% and two risk levels (high and low risk of having CVD) with an F1 score of 91%. The stacking classifier incorporating best-performing ML algorithms was used for predicting the risk levels of the end-users utilizing the UCI Repository dataset.
CONCLUSIONS: The resultant system allows the users to check and monitor their possibility of having CVD in near future using real-time data. Also, the system was evaluated from the Human-Computer Interaction (HCI) point of view. Thus, the created system offers a promising resolution to the current biomedical sector.
BACKGROUND: Not Applicable.
METHODS: From methodological perspective, a design Science Research (DSR) approach is adopted in this research. As such, to investigate the current healthcare and interaction systems created for predicting cardiac disease for patients, we first analyzed the body of existing literature. After that, a conceptual framework of the system was designed using the gathered requirements. Based on the conceptual framework, the development of different components of the system was completed. Finally, the evaluation study procedure was developed taking into account the effectiveness, usability and efficiency of the developed system.
RESULTS: To attain the objectives, we proposed a system consisting of a wearable device and mobile application, which allows the users to know their risk levels of having CVDs in the future. The Internet of Things (IoT) and Machine Learning (ML) techniques were adopted to develop the system that can classify its users into three risk levels (high, moderate and low risk of having CVD) with an F1 score of 80.4% and two risk levels (high and low risk of having CVD) with an F1 score of 91%. The stacking classifier incorporating best-performing ML algorithms was used for predicting the risk levels of the end-users utilizing the UCI Repository dataset.
CONCLUSIONS: The resultant system allows the users to check and monitor their possibility of having CVD in near future using real-time data. Also, the system was evaluated from the Human-Computer Interaction (HCI) point of view. Thus, the created system offers a promising resolution to the current biomedical sector.
BACKGROUND: Not Applicable.
摘要：
背景：尽管医疗保健领域的技术进步，心血管疾病（CVD）引起的全球疾病负担正在上升，主要是由于正在经历快速健康转型的发展中国家的急剧增加。自古以来，人们就一直在尝试延长寿命的技术。尽管如此,技术距离实现降低死亡率的目标还有很长的路要走。
方法：从方法论的角度来看，本研究采用了设计科学研究(DSR)方法。因此，调查当前为预测患者心脏病而创建的医疗保健和交互系统，我们首先分析了现有文献的主体。之后,使用收集的需求设计了系统的概念框架。基于概念框架，完成了系统不同组件的开发。最后,评估研究程序是在考虑有效性的情况下制定的，所开发系统的可用性和效率。
结果：为了实现目标，我们提出了一个由可穿戴设备和移动应用程序组成的系统，这允许用户知道他们未来患CVD的风险水平。采用了物联网（IoT）和机器学习（ML）技术来开发该系统，该系统可以将其用户分为三个风险级别（高，患有CVD的中度和低度风险），F1评分为80.4%，两种风险水平（患有CVD的高风险和低风险），F1评分为91%。合并了性能最佳的ML算法的堆叠分类器用于利用UCI存储库数据集预测最终用户的风险水平。
结论：由此产生的系统允许用户使用实时数据检查和监测他们在不久的将来发生CVD的可能性。此外，从人机交互（HCI）的角度对系统进行了评估。因此，创建的系统为当前的生物医学领域提供了有希望的解决方案。
背景：不适用。
方法：从方法论的角度来看，
结果：为了实现目标，
结论：由此产生的系统允许用户使用实时数据检查和监测他们在不久的将来发生CVD的可能性。
背景：不适用。
