Comparative Performance Analysis of Various Classifiers for Cloud E-Health Users

T. MuthamilSelvan and B. Balamurugan
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T. MuthamilSelvan: School of Information Technology & Engineering, VIT University, Vellore, India
B. Balamurugan: School of Information Technology & Engineering, VIT University, Vellore, India

International Journal of E-Health and Medical Communications (IJEHMC), 2019, vol. 10, issue 2, 86-101

Abstract: Several classifiers are prevalent which act as a major drive for almost all supervised machine learning applications. These classifiers, though their objective working looks similar, they vary drastically in their performances. Some of the important factors that cause such variations are the scalability of the dataset, dataset nature, training time estimation, classifying time for the test data, prediction accuracy and the error rate computation. This paper focuses mainly on analyzing the performance of the existing four main classifiers: IF-THEN rule, C4.5 decision trees, naïve Bayes, and SVM classifier. The objective of this research article is to provide the complete statistical performance estimates of the four classifiers to the authenticated cloud users. These users have the access facility in obtaining the essential statistical information about the classifiers in study from the cloud server. Such statistical information might be helpful in choosing the best classifier for their personal or organizational benefits. The classifiers follow the traditional underlying algorithms for classification that is performed in the cloud server. These classifiers are tested on three different datasets namely PIMA, breast-cancer and liver-disorders dataset for performance analysis. The performance analysis indicators used in this research article to summarize the working of the various classifiers are training time, testing time, prediction accuracy and error rate computation. The proposed comparative analysis framework can be used to analyze the performances of the classifiers with respect to any input dataset.

Date: 2019
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