Big Data Analytics in Healthcare using Machine Learning Algorithms: A Comparative Study

Sai Hanuman Akundi, Soujanya R, Madhuri PM


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.


Big data; Big data Analytics; Predictive Analytics; Machine Learning; Apache Spark

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International Journal of Online and Biomedical Engineering (iJOE) – eISSN: 2626-8493
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