ABSTRACT
In current era with rapid advancement in data acquisition and sensing technologies healthcare institute and hospitals have started collecting enormous data about their patients. The issue with healthcare data is that it has an unprecedented diversity in terms of its data formats, data types, and the rate at which it needs be analysed to deliver the required data. This variety is not just limited by its amount. Given the variety and number of sources that really are constantly expanding, it is now difficult to handle such vast volume of unstructured data using conventional tools and methodologies. As a result, sophisticated analytical tools and technologies are required, especially big data analytics techniques, for interpreting and managing the data related to healthcare.
Big data analytics can be used in healthcare for better diagnosis, disease prevention, telemedicine (especially when using real-time alerts for immediate care), monitoring patients at home, avoiding hospital visits, integrating medical imaging for a wider diagnosis. Hence, Healthcare data analytics have gained a lot of attention, but their pragmatic application has yet to be adequately explored. The chapter aim to cover various problems and potential solutions that frequently arise when big data analytics are utilized in medical institutions. We have reviewed big data analytical tools and methods in healthcare such as classification, clustering, artificial neural network and fuzzy.
