Abstract
Purpose
the problem of big data analytics and health care support systems are analyzed. There exist several techniques in supporting such analytics and robust support systems; still, they suffer to achieve higher performance in disease prediction and generating the analysis.. For a hospital unit, maintaining such massive data becomes a headache. However, still, the big data can be accessed towards analyzing the bio signals obtained from the human body for the detection and prediction of various diseases. To overcome the deficiency, an efficient Health Care Big Data Analytics Model (HCBDA) is presented, which maintains a massive volume of data in the data server.
Methods
The HCBDA model monitors the patients for their current state in their cardiac and anatomic conditions to predict the diseases and risks. To perform analysis on health care, the model has accessed the data location by discovering the possible routes to reach the source. The monitored results on blood pressure, temperature, and blood sugar are transferred through the list of routes available. The network is constructed with a list of sensor nodes and Internet of Things (IoT) devices, where the sensor attached to the patient initiates the transmission with the monitored results. The monitored results are transferred through the number of intermediate nodes to the monitoring system, which accesses the big data to generate intelligence. The route selection is performed according to the value of Trusted Forwarding Weight (TFW) and Trusted Carrier Weight (TCW). At each reception, the features from the packet are extracted, and obtained values are fed to the decisive support system. The decisive support system cluster the big data using the FDS clustering algorithm, and the classification is performed by measuring the feature disease class similarity (FDCS). According to the class identified, the method would calculate Disease Prone Weight (DPW) to generate recommendations to the medical practitioner.
Results
The unique Health Care Big Data Analytics (HCBDA) paradigm for patient-centered healthcare using wireless sensor networks and IoT devices was described. The patient's bio signals are watched in order to provide medical assistance. In comparison to the previous methods, the proposed approach helps to generate higher performance in disease prediction accuracy up to 96%.
Conclusion
The value of Trusted Forwarding Weight (TFW) and Trusted Carrier Weight is used to determine the route (TCW). Sensor based IoT values like Pressure glucose, pulse oximeter, and temperature etc. the following parameters like classification accuracy and false ratio are calculated based on efficient machine learning model. The crucial support system receives the values it receives after each reception together with the features that were derived from the packet. The classification is carried out by calculating the Feature Disease Class Similarity, and the decision support system clusters the huge data using the FDS clustering technique.