Abstract
In recent years vast quantities of data have been managed in various ways of medical applications and multiple organizations worldwide have developed this type of data and, together, these heterogeneous data are called big data. Data with other characteristics, quantity, speed and variety are the word big data. The healthcare sector has faced the need to handle the large data from different sources, renowned for generating large amounts of heterogeneous data. We can use the Big Data analysis to make proper decision in the health system by tweaking some of the current machine learning algorithms. If we have a large amount of knowledge that we want to predict or identify patterns, master learning would be the way forward. In this article, a brief overview of the Big Data, functionality and ways of Big data analytics are presented, which play an important role and affect healthcare information technology significantly. Within this paper we have presented a comparative study of algorithms for machine learning. We need to make effective use of all the current machine learning algorithms to anticipate accurate outcomes in the world of nursing.
Highlights
Big data on patient healthcare, compliance and numerous regulatory demands are created rapidly in all fields, including safety
In order to predict the accurate results in health care domain we have to make good use of all the above traditional machine learning algorithms
In this paper we presented a brief overview of Big Data and the features and forms of Big Data Analytics that play an important role and affect the healthcare system
Summary
Big data on patient healthcare, compliance and numerous regulatory demands are created rapidly in all fields, including safety. The method of extracting information from a wide variety of data in the brief big data analysis [14]. Predictive analytics in this industry will deliver outstanding results by improving service quality. Predictive approaches used to determine the risk of re-admission to the hospital population of patients today. Such details allow physicians to make better choices on patient treatment. Spark supports the in-memory processing mechanism that allows data to be queried much faster than diskbased drives like Hadoop, as well as general model execution that optimizes arbitrary operator graphs. [14]
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