期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2014
卷号：5
期号：4
页码：5110-5115
出版社：TechScience Publications
摘要：Classification techniques have been widely used in the medical field for accurate classification than an individual classifier. This paper presents computational intelligence techniques for Liver Patient Classification. This paper evaluates the selected classification algorithms (J-48, Multi Layer Perceptron, Support Vector Machine, Random Forest and Bayesian Network) for the classification of liver patient datasets. This paper implements hybrid model construction and comparative analysis for improving prediction accuracy of liver patients in three phases. In first phase, classification algorithms are applied on the original liver patient datasets collected from UCI repository. In second phase, by the use of feature selection, a subset (data) of liver patient from whole liver patient datasets is obtained which comprises only significant attributes and then applying selected classification algorithms on obtained, significant subset of attributes. SVM algorithm is considered as the better performance algorithm, because it gives higher accuracy in respective to other classification algorithms before applying feature selection. But, Random Forest algorithm is considered as the better performance algorithm after applying feature selection. In third phase, the results of classification algorithms with and without feature selection are compared with each other. The results obtained from our experiments indicate that Random Forest algorithm outperformed all other techniques with the help of feature selection with an accuracy of 71.8696%.
关键词：Classification; J-48; Multi Layer Perceptron;Support Vector Machine; Random Forest; Bayesian;Network; Feature Selection; Weka tool.
