Cloud-based IoMT framework for cardiovascular disease prediction and diagnosis in personalized E-health care

The advent of Internet technology has provided the opportunity to connect billions of computers and devices globally. The advantages offered by Internet technology have been extended to Internet-of-things (IoT). The extension of IoT to include devices in the medical domain with reference to Internet of medical things (IoMT) has improved the quality of personalized health-care services. However, the huge volume of big data generated by IoMT sensing devices in the health-care environment is of great concern. This has created several challenges including identification of effective techniques to mine this huge amount of data. Thus, cloud-based applications are playing significant roles in addressing secure data storage and efficient service delivery. IoT technology integrated into the cloud enhances health-care service delivery through effective resource utilization, storage, energy, and computational capability. However, despite the huge investment in the health-care industry, the potential development in patient safety and health care is not yielding significant results as many hospitals are still using paper-based medical records with handwritten documents to monitor patients’ health conditions. This makes the process of sharing patient’s information, updating, and decision-making on medical history extremely cumbersome. As a result, the spread of chronic diseases, such as cardiovascular disease (CVD), is on the high side. CVD is a variety of conditions that affect the human heart, including heart failure, hypertensive, rheumatic, abnormal heart rhythms, congenital heart disease among others. Available statistics show that heart disease rate is rising even in very wealthy countries and it remains the major contributing disease that leads to sudden death and disabilities globally. Therefore, CVDs need to be properly predicted, diagnosed, and managed. This chapter discusses the fundamental concepts of cloud computing, IoT, and IoMT. Numerous case studies on the successful implementation of these technologies in the medical domain were discussed. The challenges imposed by these technologies to patients’ health records were highlighted. The chapter further proposed a cloud-based IoMT framework for the prediction and diagnosis of CVD. Preliminary investigation of the performance of the proposed cloud-based framework to predict and diagnose real-life cases of CVD was presented. Experimental findings show that the proposed IoMT cloud-based framework for personalized health care provides an effective solution over the conventional method of physically monitoring patient heart condition at the hospital. The latter approach is cumbersome and time consuming with low coverage.