HEALTHCARE BIG DATA ANALYTICS USING BROWNBOOST CLASSIFIER BASED BLOOM HASH DATA STORAGE
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Abstract
Healthcare big data analytics involves analysis of large amounts of patient data in order to
discover useful information. There are many challenges that big data analytics presents in
many areas, including cloud healthcare systems. Healthcare industry generates significant
amounts of patient data. Recent research focuses on big data analytics-enabled models that
increase prediction accuracy and reduce patient risk. Data storage is a concern. Data must be
accessible from different locations within the distributed environment. We are studying a
BrownBoost Classifier Based Bloom Hash Data Storage (BBC–BHDS) system to store and
retrieve healthcare data from different locations in a distributed environment. This will allow
for faster access and less space usage. Initial data collection (i.e. patient data) is done based
on some parameters. The input data is then classified using the BrownBoost Classification
(BBC). BrownBoost uses a non-convex probability loss function and the base SVM classifier
to classify the patient data.
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