A Systematic Review on IoT and Machine Learning Algorithms in E-Healthcare

Abstract

In recent years, the Internet of Things (IoT) has been adopted in many applications since its usage is essential to daily life. Also, it is a developing technology in the healthcare system to provide effective emergency services to patients. In the current scenario, medical cases and diseases among people are growing enormously. Thus, it is becoming challenging to accommodate and provide healthcare services for more incoming patients in clinics and hospitals with limited space and medical resources. Hence, the integration of IoT and assistive technologies came into the healthcare sector for providing efficient healthcare services wirelessly as well as for continuous monitoring of the patients. With the help of IoT and Machine Learning technologies, healthcare providers can keep a closer eye on their patients and maintain more proactive lines of communication with them. Data collected from IoT devices can be fed to Machine Learning technologies for predicting and diagnosing diseases. Due to the severity of diseases, lack of early disease prediction methods, lack of resources, and a smaller number of specialized doctors, most of the population is dying. Hence, to address these issues in the healthcare domain, more research works are proposed based on Machine Learning and IoT-based healthcare systems. This work reviews the research works related to IoT-based healthcare systems and machine learning comprehensively.