Voting-Based Extreme Learning Machine Approach for the Analysis of Sensor Data in Healthcare Analytics

There has been a huge surge in the production of sensor-based clinical devices for health monitoring systems over the last few years. This sudden spike has been due to many different factors such as development in sensor device technology and also the efforts to promote research that address the necessity for providing new ways for healthcare given the increasing challenges with an associated degree of an aging population. The processing and analysis of data is an important element of the research of such a system. The data generated from these healthcare devices are enormous and have the potential to ascertain well-being and to encourage effective management of health. In this work, a mechanism for the analysis of physiological sensor data from the healthcare devices, namely voting-based extreme learning machine, has been explored. The approach was also compared with the traditional extreme learning machine-based approach. Experimental results were very encouraging with respect to the performance accuracy as well as time taken by the voting-based extreme learning machine as compared to the traditional extreme learning machine to produce the output.