Advanced method for the Analyses of Large Amounts of Data using deep learning in Health Sector

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Dr. Vidya Gavekar, Dr. Deepti P Lele, Dr.Manisha Kumbhar


Large amounts of data, rising costs, and a focus on personalised care have all led to a rise in the use of "big data" in healthcare over the past few years. "Big data processing" is a term used in healthcare to describe the development, collection, analysis, and storage of clinical data that is too big or complicated to be figured out with typical data processing methods. Big data sources for healthcare include the Internet of Things (IoT), Electronic Medical Records/Electronic Health Records (EMR/EHR), which include a patient's medical history, diagnosis, treatment plans, allergies, and lab and test results, and genomic sequencing. In this article, a wide range of healthcare data was analysed using machine learning. As well as the fact that it is hard to gather, handle, and analyse a lot of data. In this paper, we'll take a fresh look at how machine learning algorithms and the need to analyse and use huge amounts of data are linked in real life. Deep learning can be used to look at a lot of data in the health field.

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