Comparitive study on healthcare prediction systems using big data

The era of digitization has led computers to become the real face of handling commercial processes across a plethora of industries. As institutions today specifically in the medical domain have resorted to these virtual machines for realizing their goals, more and more medical data is being generated on a continuous basis. This data is being used in many recommendation systems which deliver a personalized individual's health profile model [1]. As a result, a colossal amount of data has been accumulated and is being continuously dumped by the medical organizations. This trend has led to the emergence of big data analytics which is being adopted by these recommendation systems in order to enhance predictive analysis foreseeing its huge upside potential in the healthcare sector. The paper discusses the transitions being observed in the healthcare domain which require the use of big data to make some useful predictions based upon analysing a variety of datasets and comparing their similar features. A detailed comparison amongst the existing prediction based algorithms is given and their key features are highlighted. Finally, the work illustrates a scope of a profile set comprising of various noteworthy implications observed from these models and discusses the benefits of big patient data. The profile set may denote a stepwise analysis of the patients health based upon various parameters, and also give a framework for effective treatment strategies.