A comparative study of fuzzy classifiers on heart data

Author

Anushya, A. ; Pethalakshmi, A.

Author_Institution

Dept. of Comput. Sci., Manonmaniam Sundaranar Univ., Tirunelveli, India

fYear

2011

fDate

8-9 Dec. 2011

Firstpage

17

Lastpage

21

Abstract

Fuzzy approaches can play an important role in data mining, because they provide comprehensible results. In addition, the approaches studied in data mining have mainly been oriented at highly structured and precise data. In this paper, we examine the performance of four fuzzy classifiers on heart data. The fusion of Fuzzy Logic with the classifiers Decision Trees, K-means, Naïve bayes and neural network are used to evaluate the accuracy of occurrence of a heart disease. The experiments are carried out on heart data set of UCI machine learning repository and it is implemented on MATLAB.

Keywords

Bayes methods; data mining; decision trees; diseases; fuzzy logic; fuzzy set theory; learning (artificial intelligence); medical computing; neural nets; pattern classification; MATLAB; UCI machine learning repository; data mining; decision trees classifier; fuzzy classifiers; fuzzy logic fusion; heart data set; heart disease; k-means classifier; naïve Bayes classifier; neural network classifier; Accuracy; Levee; MATLAB; Data mining; Fuzzy K-means; Fuzzy Naïve bayes; Fuzzy Neural Network; Fuzzy decision trees; Fuzzy logic;

fLanguage

English

Publisher

ieee

Conference_Titel

Trendz in Information Sciences and Computing (TISC), 2011 3rd International Conference on

Conference_Location

Chennai

Print_ISBN

978-1-4673-0134-3

Type

conf

DOI

10.1109/TISC.2011.6169077

Filename

6169077