ABSTRACT
The healthcare environment is driving enterprises towards a new health IT infrastructure strategy. Organizations must invest in new IT that can meet the needs of hospitals and patients as data volumes grow every day. This is required as hospitals demand real-time access to critical diagnostic information that can improve care quality. Centers for Medicare & Medicaid Services have made significant alterations to the way healthcare providers generate, store, and analyze digital data. A large amount of data is now easily available on the Web and is generally heterogeneous and unstructured. Machine Learning (ML) techniques can be applied to automatically retrieve, classify or cluster observations on large data. Big Data (BD) analytics has led to many recent initiatives in both theory and practice and has inspired the interest in the ML community. Developing prediction models for BD problems with ML as a service is poised to change Healthcare Analytics toward a better future and is the next big thing in the healthcare industry as hospitals start to adopt advanced data analytics capabilities. Since BD is associated with high variety, variable, velocity, IoT data, advanced ML techniques are required to generate knowledge that can be used to improve results in the process of delivering patient care.
