IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Usage of Big Data Prediction Techniques for Predictive Analysis in HIV/AIDS

Usage of Big Data Prediction Techniques for Predictive Analysis in HIV/AIDS
View Sample PDF
Author(s): Chinmayee Mohapatra (KIIT University, India), Biswaranjan Acharya (KIIT University, India), Siddhath Swarup Rautaray (KIIT University, India) and Manjusha Pandey (KIIT University, India)
Copyright: 2018
Pages: 27
Source title: Big Data Analytics in HIV/AIDS Research
Source Author(s)/Editor(s): Ali Al Mazari (Alfaisal University, Saudi Arabia)
DOI: 10.4018/978-1-5225-3203-3.ch003

Purchase

View Usage of Big Data Prediction Techniques for Predictive Analysis in HIV/AIDS on the publisher's website for pricing and purchasing information.

Abstract

The term big data refers to the data that exceeds the processing or analyzing capacity of existing database management systems. The inability of existing DBMS to handle big data is due to its large volume, high velocity, pertaining veracity, heterogeneous variety, and on-atomic values. Nowadays, healthcare plays a vital role in everyone's life. It becomes a very large and open platform for everyone to do all kinds of research work without affecting human life. When it comes to disease, there are so many types found all over the world. But among them, AIDS (acquired immunodeficiency syndrome) is a disease that spreads so quickly and can easily turn life to death. There are many studies going on to create drugs to cure this deadly disease, but until now, there has been no success. In cases such as this, big data is implemented for better a result, which will have a good impact on society.

Related Content

Giulia Perasso, Chiara Baghino, Elisabetta Capris, Elena Cocchi, Silvia Dini, Valentina Facchini, Antonella Panizzi, Valentina Salvagno. © 2022. 24 pages.
Simon Shachia Oryila, Philip Chike Chukwunonso Aghadiuno. © 2022. 32 pages.
Macire Kante, Patrick Ndayizigamiye. © 2022. 17 pages.
Abiodun Alao, Roelien Brink. © 2022. 32 pages.
Patrick Ndayizigamiye. © 2022. 19 pages.
Mwai Chipeta, Donald Flywell Malanga. © 2022. 28 pages.
Margaret Nagwovuma, Gilbert Maiga, Agnes Nakakawa, Emmanuel Eilu. © 2022. 22 pages.
Body Bottom