Title :
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
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;
Conference_Titel :
Trendz in Information Sciences and Computing (TISC), 2011 3rd International Conference on
Conference_Location :
Chennai
Print_ISBN :
978-1-4673-0134-3
DOI :
10.1109/TISC.2011.6169077
