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Big data, big knowledge: big data for

personalised healthcare

ABSTRACT

We propose in this position paper that big data analytics can be successfully combined with VPH technologies to produce robust and effective in silico medicine solutions. In order to do this, big data technologies must be further developed to cope with some specific requirements that emerge from this application. Such requirements are: working with sensitive data; analytics of complex and heterogeneous data spaces, including non-textual information; distributed data management under security and performance constraints; specialised analytics to integrate bioinformatics and systems biology information with clinical observations at tissue, organ and organisms scales; and specialised analytics to define the “physiological envelope” during the daily life of each patient.

EXISTING SYSTEM	PROPOSED SYSTEM
EXISTING SYSTEM:- The idea that the purely phenomenological knowledge that we can extract by analysing large amounts of data can be useful in healthcare seems to contradict the desire of VPH researchers to build detailed mechanistic models for individual patients. But in practice no model is ever entirely phenomenological or entirely mechanistic.	PROPOSED SYSTEM:- We propose in this position paper that big data analytics can be successfully combined with VPH technologies to produce robust and effective in silico medicine solutions. In order to do this, big data technologies must be further developed to cope with some specific requirements that emerge from this application.
EXISTING ALGORITHM :- Apriori algorithm	PROPOSED ALGORITHM :- Clustering algorithm.
ALGORITHM   DEFINITION:- An Efficient Technique on  Apriori Based Master Slave Architecture Design, the hybrid approach was formed which consists of both top down and bottom up approach. This hybrid approach when compared with the Novel and Apriori algorithm.	ALGORITHM DEFINITION:- Clustering can be considered the most important unsupervised learning problem; so, as every other problem of this kind, it deals with finding a structure in a collection of unlabeled data.
DRAWBACKS:- Due to Increase in the amount of data in the field of genomics, meteorology, biology, environmental research, it becomes difficult to handle the data, to find Associations, patterns and to analyze the large data sets.	ADVANTAGES:- Modern big data technologies make it possible in a short time to analyze a large collection of data from thousands of patients, identify clusters and correlations, and develop predictive models using statistical or machine-learning modeling techniques