Performance Analysis of Predictive Association Rule Classifiers using Healthcare Datasets. Issue 1 (2nd January 2022)

• Record Type: Journal Article
• Authors: Nandhini, M.; Rajalakshmi, M.; Sivanandam, S. N.
• Is Part Of: IETE technical review. Volume 39:Issue 1(2022)
• Page Start: 143
• Page End: 156
• Publication Date: 2022-01-02
• Subjects: Association rules -- Classification algorithms -- Data mining -- Gain ratio -- Information entropy -- Mutual information; Telecommunication -- Periodicals; Electronics -- Periodicals; Electronics; Telecommunication; Periodicals; 621.38
• DOI: 10.1080/02564602.2020.1827988
• Languages: English
• ISSNs: 0256-4602

Abstract

Abstract : In recent years, the use of data mining techniques has gained significance in healthcare applications. The appropriate data mining techniques extract interesting affinities/associations between patient's signs and symptoms, thus providing reasonable decision-making for the diagnosis and prognosis of the disease. Associative Classification (AC) is a contemporary technique, which uses the Class Association Rules (CARs) to build the classification system. Classification based on Predictive Association Rule (CPAR) is one of the popular AC algorithms, which utilise FOIL's Information Gain measure to select the best attributes for the generation of CARs. This work attempts to explore a suitable attribute selection measure and an error estimate measure that can best fit into the existing CPAR algorithm to construct an efficient rule-based classifier. Thus the performance of CPAR has been analyzed by applying alternative attribute selection measures such as Gain Ratio (GR) and Mutual Information Gain (IG) instead of FOIL's Information Gain. Moreover, two error estimate measures such as Laplace accuracy ("La") and Likelihood ratio statistic ("Lr") are used for rule evaluation and best k-rule selection tasks of CPAR. This work analyzes the performance of CPAR -GR and CPAR -IG with the existing CPAR algorithm in terms of classifier accuracy. From the results, it was found that the use of " GR " and " IG " within CPAR yields accuracy higher than the existing CPAR. Significant differences in the performances of CPAR, CPAR- GR, and CPAR- IG are identified and demonstrated by experiments, and statistical tests using healthcare datasets taken from the UCI machine learning repository.