Application of Artificial Immune System Algorithms on Healthcare Data

Abstract

Data mining is one of the most significant ways of extracting the important information from a required set of data. Now-a-days the healthcare systems generate a very large amount of data, which are difficult to analyze through traditional methods. Data mining techniques provide the technology to extract meaningful information from these huge healthcare data for decision making. This paper mainly focuses on the analysis and evaluation of different parameters from large healthcare datasets, using Artificial Immune System (AIS) based classification algorithms, and normal classification algorithms. Five life science based datasets focusing on healthcare are considered for our experiment, to evaluate different parameters, using AIS based and normal classification algorithms. The result of the experiment is analyzed to propose the best classifier among the considered algorithms, based on the factors like accuracy, sensitivity, F-measure and specificity. The proposed classifier can further be used for different decision making purposes in healthcare systems.