Application of Data Analytics Principles in Healthcare
R Arokia Paul Rajan
Dr. R Arokia Paul Rajan, Associate Professor, Department of Computer Science, CHRIST Deemed to be University, Bangalore (Karnataka), India.
Manuscript received on 18 October 2019 | Revised Manuscript received on 25 October 2019 | Manuscript Published on 02 November 2019 | PP: 3151-3155 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B14110982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1411.0982S1119
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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: Information technology has transformed the healthcare field worldwide. In many areas of the healthcare industry, implementations of data analytics tools are commonly used recently. Applying data analytics principles in medical sciences appropriately transforms the mere storage of medical records in to discovery of drugs. Data science and analytics are essential tools because they can help make better decisions when it comes to spending and reducing inefficiencies in healthcare. The proposed model of healthcare data analytics provides a framework to accelerate the adoption and implementation of predictive analytics in healthcare. Healthcare data analytics can be applied to prove formulated hypotheses, test those using standard analytics models and predict patient health conditions. It can be used to classify patients at risk of developing diseases such as diabetes, asthma, and other life-long illnesses. In spite of the challenges faced while applying data science predictive analytics in the healthcare environment, there is an enormous opportunity for its usage in providing quality healthcare for patients.
Keywords: Healthcare, Data Mining, Data Analytics, Predictive Analytics, Healthcare Informatics.
Scope of the Article: Data Analytics