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