A Systematic Review On Disease Prediction In Big Data With Optimization And Map Reduce Framework

  • K.Manohari ,Dr.S.Manimekalai

Abstract

The vast amount of health data continues to expand every second, making discovering some sort of valuable knowledge harder and very challenging. Traditional way of providing data, particularly in health sector, has recently been changed by big data into useful insights. It offers a wide variety of preventive advantages for initial diagnosis of critical illnesses and provides quality health care to right patient. It has created resources for rapid collection, management, review and assimilation of large amounts of disparate, organized and unstructured vital data generated by various storage systems of health information. In existing health data analytics systems, though, there are many problems to be tackled that provide strategic methods such as critical data collection, aggregation, operation, interpretation, simulation, and sharing. Due to lack of detailed analysis in existing research works, this paper examines available methods in terms of 3 categories such as disease prediction, optimization algorithm and Hadoop map reduce framework in big data with their comparative analysis. This systematic review will provide a clear idea for researchers about various methods for particular disease prediction in big data.

Published
2021-01-01
Section
Articles