Design and implement a novelframework for big data analysis in Healthcare

Abstract

Big data extends its horizon to various applications. Health care is not an exception to this. Automation of health care industry, new tools and techniques for collecting the health features such as the sensors and the body area network paves way for the generation of large amount of data. At the other side generating large amount of data in healthcare also become necessary in order to make a better analysis and the prediction with the health care data. In order to justify the necessity of the big data environment in the health care environment, experimentation is made with the body area network and data are collected from the patients. Various sensors are used and different health parameters are obtained. The data are summarized and it has been observed from the results that the volume of the data generated is considerably high. But, handling such large data is not an easier task. It needs specialized environment such as the cloud environment and the big data context. A cloud computing framework is designed. It enables an efficient data transmission mechanism from the point of data collection in the hospital to the cloud data centre. This data transformation model includes few mechanisms for better performance. It includes, a window based mechanism for better data collection. Simulated annealing mechanism for finding the optimal parameter of the individual health features. In addition to these two mechanisms, a critical bit is also used in order to assigning priority to the patients observed with abnormality and serves them better. Two other approaches are also used for finding the inter cluster correlation and the intra cluster correlation. The sub processes included in finding the cluster correlation is made in the map reduce framework in order to improve the performance with parallelization. newline