The Internet of Things and Big Data Analytics for Chronic Disease Monitoring in Saudi Arabia.

Background: Saudi Arabia is lagging behind developed countries in devising specific real projects, roadmaps, and policies for the Internet of Things (IoT) and big data adoption despite having a vision for providing the best-quality health care services to its citizens. As a result, Saudi Arabia is going to host an event for the third time, in 2020, promoting the widescale adoption of the IoT. While a nationwide study has identified the risk that many participants were previously undiagnosed for hypertension and other chronic diseases in Saudi Arabia, the application of the IoT and big data technologies could be very useful in minimizing such risks by predicting chronic disease earlier, and on a large scale. Materials and Methods: A framework that consists of four modules, (1) data collection, (2) data storage, (3) Hadoop/Spark cluster, and (4) Google Cloud, was developed in which decision tree and support vector machine (SVM) techniques were used for predicting hypertension. There were 140 participants in total and 20% of participants were used for training the model. Results: The results show that age and diabetes play a very significant part in diagnosing hypertension in older people. Also, it was found that the possibility of hypertension because of smoking is less than that of diabetes, and older people should have a lower intake of salty food. Moreover, it was found that SVM techniques yielded better results than C4.5 in our study. Conclusions: Although it was found that the algorithms examined in this study can be used for disease prediction, the ability to classify and predict disease is not yet sufficiently satisfactory. To achieve this, more training data and a longer duration are required. Finally, by supporting such study for developing custom-made smart wristbands, custom-made smart clothing, and custom-made smart homes that can predict and detect a wide range of chronic diseases, the Saudi government can achieve its health-related goals of Vision 2030.

Big Data Based m-Health Application to Prevent Health Hazards: A Design Science Framework.

BACKGROUND: Every year about three million Muslims visit the Holy City of Makkah in Saudi Arabia to perform the Hajj. Because of the large number of people present during this period, pilgrims can be subjected to many health hazards. An adequate system to minimize these health hazards is needed to support the pilgrims who attend the Hajj. This study justifies the need for developing a large data-based m-Health application to identify the health hazards encountered during the Hajj. MATERIALS AND METHODS: In developing a big data-based m-Health application, this study follows the framework suggested by Hevner. The design of the science framework allows the development of a technological solution (i.e., design artifact) of the problem through a series of actions. The design involves rigorous knowledge of the environmental factors, including knowledge of the construction and evaluation of technological solutions, that are important and relevant to an existing problem. RESULTS: Based on the design science framework, the process of artifact development can be classified into Artifact Design, Artifact Implementation, and Artifact Evaluation. This paper presents the Artifact Design step for the design of the big data-based m-Health application, which has an Environmental Relevance Cycle, a Knowledge-based rigor Cycle, and an Artifice development and design cycle. The big data-based m-Health application is a prototype and must be evaluated using the evaluation-and-feedback loop process until the optimum artifact is completely built and integrated into the system. CONCLUSION: Development of a big data-based m-Health application using a design science framework can support the effective and comprehensive plan of the government of Saudi Arabia for preventing and managing Hajj-related health issues. Our proposed model for developing and designing a big data-based m-Health application could provide direction for developing the most advanced solution for dealing with the Hajj-related health issues in the future.