Privacy Preservation And Performance Improvement of HealthCare Data Prediction and Storage Using Map-Reduce Framework

Big data is observed as the state-of-the-art-concern which is employed to deal with immense range of data in definite fields like remote sensing, medical, customer services and so on. In recent days, health care industry has been originating vast expansion to develop individual specialized techniques. After all it is mandatory to protect the confidential patient’s medical record as it is stored in the untrusted cloud which in turn continual to concede with users according to their requirements. Despite, sustaining the ownership of an entire medical records is the crucial issueamong patient and the hospital thereby the primary responsibility of the hospital is to maintain the records precisely. Hence, the medical records of patients are not only accessed in any emergency issues or treatment. Furthermore, Big Data classification is typically a tiresome and extended task. The Map Reduce Framework technique offers the processing of data which is being paralleled through the distribution of data in smaller chunks over clusters. The technique of Map Reduce is being presented which is utilized for processing data items that are heterogeneous. By means of the well-known framework Map reduce issues and challengesof existing methods could be solved randomly. This research paper attempts pragmatic method of disease prediction and classification using discrete multilayer perceptron-based classification. Subsequently, the proposed technique performs preprocessing using Normalization, Adaptive Shearlet polynomial component analysis-based features extraction, optimization of extracted features using recurrent chicken swarm optimization algorithm, classification and prediction. Ultimately, the medical information is stored in cloud server byperforming encryption to the data using semantic polynomial based blowfish encryption