Adaptive Neural Network Classifier-Based Analysis of Big Data in Health Care

Abstract

Because of the massive volume, variety, and continuous updating of medical data, the efficient processing of medical data and the real-time response of the treatment recom-mendation has become an important issue. Fortunately, parallel computing and cloud computing provide powerful capabilities to cope with large-scale data. Therefore, in this paper, a FCM based Map-Reduce programming model is proposed for the parallel com- puting using AANN approach. The FCM based Map-Reduce, clusters the large medical datasets into smaller groups of certain similarity and assigns each data cluster to one Mapper, where the training of neural networks are done by the optimal selection of the interconnection weights by Whale Optimization Algorithm (WOA). Finally, the Reducer reduces all the AANN classifiers obtained from the Mappers for identifying the normal and abnormal classes of the newer medical records promptly and accurately. The pro- posed methodology is implemented in the working platform of JAVA using CloudSim simulator. memory. The proposed FCM based Map-Reduce model decreases the requirement of memory while equating with other accomplishing k-means based Map-Reduce and DBSCAN method.