Healthcare Prediction and Analysis System with Constant Data Polling 
Thejas Bharadwaj1, Aatish Balaji2
1Thejas Bharadwaj, Computer Science Engineering, Vellore Institute of Technology, Vellore, India.
2Aatish Balaji, Computer Science Engineering, Vellore Institute of Technology, Vellore, India.
Manuscript received on September 15, 2020. | Revised Manuscript Received on September 21, 2020. | Manuscript published on September 20, 2020. | PP: 1-8 | Volume-5 Issue-11, September 2020. | Retrieval Number: 100.1/ijies.K09930751120 | DOI: 10.35940/ijies.K0993.0951120
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© The Authors. Published By: 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: The applications of Big data and machine learning in the fields of healthcare, bioinformatics and information sciences are the most important things that a researcher takes into consideration when doing predictive analysis. The Data production at this stage has never been higher and it is increasing at an alarming rate. Hence, it is difficult to store, process and visualise this huge data using customary technologies. However, abstract design for a specific massive information application has been restricted. With advancement of big data in the field of biomedical and healthcare domain, accurate analysis of medical data can be proved beneficial for early disease detection, patient care and community services. Machine learning is being used in a wide scope of application domains to discover patterns in huge datasets. Moreover, the results from machine learning drive critical decisions in applications relating healthcare and biomedicine. The transformation of data to actionable insights from complex data remains a key challenge. In this paper we have introduced a new method of polling of data before analysis is conducted on it. This method will be valuable for dealing with the issue of incomplete data and will progressively prompt suitable and more precise data extraction.
Keywords: Machine Learning; Data Polling; Data Mining; Healthcare; Customer Relation Management (CRM); Prediction System; Cloud Computing; Data Analysis;