Efficient Detection Using Soft Computing Approach of Modified Fuzzy C-Means Based Outlier Detection in Electronics Patient Records Systems

Abstract

Outliers are the data objects or data elements which are deviated from the observation or population in the dataset. Normally outliers are considered as noise data but in recent days outliers are taken vital role in the applications like healthcare systems to energetically check for any malignant, irregular, or abnormal behaviour. Accuracy of the outlier detection is purely based upon the efficiency of outlier detection methods and application where the outlier detection is involved. In this paper we design hybrid three phase modified fuzzy c-means and diverse distance based outlier detection method for distributed dataset to detect the unusual usage of data and illegitimate approach in large-scale integral networks, especially in healthcare sectors (EPR systems). The proposed algorithm combining the features of modified C-Means Fuzzification, Z-score and Manhattan distance in outlier identification. The proposed algorithm provides efficiency in outlier detection on univariate EPRdata. This paper also mainly focuses the application intrusion detection in healthcare data. The algorithm is tested on real world dataset of machine learning database repository (UCIML).