Predictive Health Analytic Model in Federated Cloud
A. Karunamurthy1, M. Aramudhan2
1Karunamurthy A, Research Scholar, Department of Computer Science, Research and Development Centre, Bharathiar University, Coimbatore, India.
2Dr.M. Aramudhan, Associate Professor & Head, Department of Information Technology, Perunthalaivar Kamarajar Institute of Engineering & Technology, Karaikal, India.
Manuscript received on 07 March 2019 | Revised Manuscript received on 14 March 2019 | Manuscript published on 30 July 2019 | PP: 2093-2096 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2309078219/19©BEIESP | DOI: 10.35940/ijrte.B2309.078219
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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 Health Analytic is a challenging discipline in healthcare industry where knowledge can be transferred into action. The basic steps in predictive modeling are to define the problem, gather the initial necessary data and evaluate several different algorithm approaches. Later his process to be refined by selecting best performing models, testing with bench mark data sets and real world setting. Predictive analytics helps to extract useful knowledge and support in making decisions. In this paper, federated health providers are interconnected by using brokers, gather information and helps in decision making related to the issues of health. Each provider has provided the awareness about the distinct diseases, predict the possible level of diseases affected and the mode of treatment. Simulation result reveals that the proposed architecture is essential for the present needs of human life.
Keywords: Health Analytics, Predictive, Federated Cloud, Fuzzy Models, Regression Models, Bench Mark Data, Decision Making.
Scope of the Article: Healthcare Informatics