Evaluation of Various DR Techniques in Massive Patient Datasets using HDFS

The objective of comparing various dimensionality techniques is to reduce feature sets in order group attributes effectively with less computational processing time and utilization memory. reduction algorithms can decrease the dataset consisting a huge number interrelated variables, while retaining dissimilarity present as much possible. In this paper we use, Standard Deviation, Variance, Principal Component Analysis, Linear Discriminant Factor Positive Region, Information Entropy Independent Analysis using Hadoop Distributed File System for massive patient datasets achieve lossless data acquire required knowledge. experimental results demonstrate that ICA technique efficiently operate on eliminates irrelevant without loss accuracy, reduces storage space also computation compared other techniques.