A Comparative Analysis of Machine Learning Algorithms For Healthcare Device Data of Social IoT
Year: 2019
Type: conference-paper
Abstract: In recent times, Social IoT(SIoT) plays a important role in everyday life. These devices communicate between each other and generate various data, in which, few healthcare devices helps in monitoring the activities of human and keep track about the health. Data accumulated from these devices are analyzed based on the sensitivity of device that adds a weightage in under- standing the criticality of the person‘s health. Many different machine learning models exists for discerning the imminence of health. Hence, in this paper, a comparative study of several machine learning algorithms that are used for classification of data are contrived. The experiment have been conducted using R Programming platform and healthcare dataset. The result shows Naive bayes model performs better with both time and space complexities for enormous healthcare data.
Authors: D. Bhavya, D. S. Vinod, S. P. Shiva Prakash
Institution: JSS Science and Technology University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W3213149851