Data Science Algorithms and Techniques for Smart Healthcare Using IoT and Big Data Analytics

Abstract

Smart healthcare network is an innovative process of synergizing the benefits of sensors, Internet of things (IoT), and big data analytics to deliver improved patient care while reducing the healthcare costs. In recent days, healthcare industry faces vast challenges to save the data generated and to process it in order to extract knowledge out of it. The increasing volume of healthcare data generated through IoT devices, electronic health, mobile health, and telemedicines screening requires the development of new methods and approaches for their handling. In this chapter, we briefly discuss some of the healthcare challenges and big data analytics evolution in this fast-growing area of research with a focus on those addressed to smart health care through remote monitoring. In order to monitor the healthcare conditions of an individual, support from sensor and IoT devices is essential. The objective of this study is to provide healthcare services to the diseased as well as healthy population through remote monitoring using intelligent algorithms, tools, and techniques with faster analysis and expert intervention for better treatment recommendations. The delivery of healthcare services has become fully advanced with integration of technologies. This study proposes a novel smart healthcare big data framework for remotely monitoring physical daily activities of healthy and unhealthy population. The framework is validated through a case study which monitors the physical activities of athletes with sensors placed on wrist, chest, and ankle. The sensors connected to the human body transmit the signals continuously to the receiver. On the other hand, at the receiver end, the signals that are stored and analyzed through big data analytics techniques and machine learning algorithms are used to recognize the activity. Our proposed framework predicts whether the player is active or inactive based on the physical activities. Our proposed model has provided an accuracy of 99.96% which can be adapted to remotely monitor health conditions of old patients in case of Alzheimer’s disease by caregivers, rehabilitation, obesity monitoring, remotely monitoring of sports persons physical exertion, and it can also be beneficial for remotely monitoring chronic diseases which require vital physical information, biological, and genetic data.