This paper is published in Volume-4, Issue-3, 2018
Area
Health Care
Author
K Sriram
Co-authors
Ravilla Raviteja, Manjunath C R, Sahana Shetty
Org/Univ
School of Engineering and Technology Jain University (SET JU), Bengaluru, Karnataka, India
Pub. Date
05 May, 2018
Paper ID
V4I3-1275
Publisher
Keywords
Kidney related diseases, Big data analytics, Predictive analytics, Machine learning techniques.

Citationsacebook

IEEE
K Sriram, Ravilla Raviteja, Manjunath C R, Sahana Shetty. Predicting kidney related diseases, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
K Sriram, Ravilla Raviteja, Manjunath C R, Sahana Shetty (2018). Predicting kidney related diseases. International Journal of Advance Research, Ideas and Innovations in Technology, 4(3) www.IJARIIT.com.

MLA
K Sriram, Ravilla Raviteja, Manjunath C R, Sahana Shetty. "Predicting kidney related diseases." International Journal of Advance Research, Ideas and Innovations in Technology 4.3 (2018). www.IJARIIT.com.

Abstract

Healthcare industry is observing a tremendous advancement along with upcoming innovations in the Information Technology and the Computer Science, and this is something which thrived this industry to come up with more added medical related information, which led to growth in several research sectors. Various steps are taken to handle the outburst of information related to medical sciences and acquisition of valuable knowledge. This eventually led the researchers and scientists towards applying their technical revolutions as well as inventions such as “predictive analytics”, “machine learning”, “big data analytics” and “learning algorithms” for gathering worthwhile understanding and support in better decision making. Big data analytics can be seen as one of the major emerging sides in the field of medical sciences. Big data is also being used for providing predictive intuitions in a healthcare field and it is also playing an important role in the analysis of chronic diseases and medical data with the help of predictive analytics. In healthcare industries prediction can turn out to be most useful as well as successful when the knowledge can be conveyed as action. In this case, we propose a method that gives real-time analyzed report about predicting kidney related diseases with the help of historical data and real-time data.
Paper PDF