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Machine Learning Approaches over Big Data for Health Care Systems: A review

Today the digital data that world produces is unseen and spectacular. The data from social media, e-commerce and Internet of things generate approximately 2.5 quintillion of bytes per day. This amount is equals 100 million Blu-ray discs or almost 30,000 GB per second. Till today data is growing and will continue to grow in future. In the field of Health care industry, big data has opened new ways to acquire intelligence and data analysis. Collected records from patient, hospital, doctors, medical treatment is known as health care big data. Big data by machine learning are assembled and evaluates the large amount of data in health care. Analytic process and business intelligence (BI) is growing up day by day, as it acquires knowledge and makes right decision. As it is vast and complex growing data, it is very difficult to store. The tradition method of handling big data is incapable to manage and process big data. Hence to resolve this difficulty, some machine learning tools are applied on large amount of data using big data analytics framework. Researchers have proposed some machine learning approaches to improve the accuracy of analytics. Each technique is applied, and their results are compared. And this concluded that we get accurate result from one machine learning approach are called as Ensemble Learning. The final result observed that ensemble learning can obtain high accuracy. In this paper we shall study about various methods to process big data for machine learning and its statistic approaches. Further we study various tools for storing of big data, its advantages, and disadvantages in the field of health care industry.