Analysis on E Healthcare Monitoring System with Iot and Big Patient Data

Abstract

Internet of Things (IoT) technology helped the development of healthcare from face-to-face consulting to the telemedicine. Smart healthcare system in IoT environment monitored the patient basic health signs such as heart rate, body temperature, and hospital room condition in real-time applications. The IoT and big data is an important challenge in many fields including smart healthcare systems due to its significance. Big data is employed to analyse the huge volume of data. Big data are significantly used in healthcare technique to determine the normal and abnormal patient condition. The doctors are easily analysed the patient condition in a short time. This system is very easy to design and use. It is employed to enhance the present healthcare system which preserves the lot of lives from death. Healthcare monitoring system in hospitals has experienced large development and portable healthcare monitoring systems with new technologies. Connected healthcare is an essential solution for hospital to record and analyse the patient data and to save money. The clustering and classification methods are used in existing methods. The clustering method is employed to group the similar data. The classification method is utilized to classify the patient data. A lot of healthcare technique was introduced by many researchers ranging from diagnosis to treatment and prevention on efficient e-health monitoring system. But, the accuracy level was not improved and time consumption was not reduced by existing techniques. In order to address these problems, different methods and techniques were reviewed for performing the e-healthcare monitoring system with big data. The machine learning techniques are used for efficient diseased patient health monitoring through the effective performance of feature selection, clustering and patient classification with increase the accuracy and minimum time consumption. The results are is performed using on different factors such as clustering accuracy, clustering time, classification accuracy, classification time, and error rate with respect to number of patient data.