Predictive Risk-Assessment System (PRAS) Platform for Healthcare Analytics and Visualization
G.Jaya Lakshmi1, Dr.Sangeetha Yalamanchili2
1G. Jaya Lakshmi, Assistant Professor, Department of Information Technology, V R Siddhartha Engineering College, Vijayawada (Andhra Pradesh), India.
2Dr. Sangeetha Yalamanchili, Associate Professor, Department of Information Technology, V R Siddhartha Engineering College, Vijayawada (Andhra Pradesh), India.
Manuscript received on 29 May 2019 | Revised Manuscript received on 11 June 2019 | Manuscript Published on 22 June 2019 | PP: 899-902 | Volume-8 Issue-3S, February 2019 | Retrieval Number: C11900283S19/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Predictive modeling assumption and implementation has established accomplishment in online business intelligence and is at present affecting from theory into practical implementation in healthcare. In the advancement of expansion for health care applications, clinical decision frameworks (CDFs) were intended to advise practitioners of probable on contraindication. CDFs are proposed to progress care and decrease outlay. The framework is implanted into online healthcare record software. The idea was to develop a predictive model for in sequence systems to recognize patients at improved hazard for chronic diseases and to vigilant medicinal professionals to take necessary precautionary actions. And the level of healthcare data has grown extremely in recent years, raising the need to present the data in a way that are more comprehensible and perceptive.
Keywords: Healthcare, Machine learning, Predictive Analytics, Ehrs, Big Data.
Scope of the Article: Predictive Analysis