Development of evolutionary data mining algorithms and their applications to cardiac disease diagnosis

Author

Liu, Jenn-Long ; Hsu, Yu-Tzu ; Hung, Chih-Lung

Author_Institution

Dept. of Inf. Manage., I-Shou Univ., Kaohsiung, Taiwan

fYear

2012

fDate

10-15 June 2012

Firstpage

1

Lastpage

8

Abstract

This paper presents two kinds of evolutionary data mining (EvoDM) algorithms, termed GA-KM and MPSO-KM, to cluster the dataset of cardiac disease and predict the accuracy of diagnostics. Our proposed GA-KM is a hybrid method that combines a genetic algorithm (GA) and K-means (KM) algorithm, and MPSO-KM is also a hybrid method that combines a momentum-type particle swarm optimization (MPSO) and K-means algorithm. The functions of GA-KM or MPSO-KM are to determine the optimal weights of attributes and cluster centers of clusters that are needed to classify the disease dataset. The dataset, used in this study, includes 13 attributes with 270 instances, which are the data records of cardiac disease. A comparative study is conducted by using C5, Naïve Bayes, K-means, GA-KM and MPSO-KM to evaluate the accuracy of presented algorithms. Our experiments indicate that the clustering accuracy of cardiac disease dataset is significantly improved by using GA-KM and MPSO-KM when compared to that of using K-means only.

Keywords

Bayes methods; cardiology; data mining; diseases; evolutionary computation; medical diagnostic computing; particle swarm optimisation; pattern classification; pattern clustering; C5; cardiac disease diagnosis; dataset clustering; diagnostics accuracy prediction; disease dataset classification; evolutionary data mining algorithm; genetic algorithm-K-means algorithm; hybrid method; momentum-type particle swarm optimization; naïve Bayes; Accuracy; Cardiac disease; Clustering algorithms; Data mining; Genetic algorithms; Heart; Prediction algorithms; Evolutaionary data mining; K-means algorithm; cardiac disease; genetic algorith; momentum-type particle swarm optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation (CEC), 2012 IEEE Congress on

Conference_Location

Brisbane, QLD

Print_ISBN

978-1-4673-1510-4

Electronic_ISBN

978-1-4673-1508-1

Type

conf

DOI

10.1109/CEC.2012.6256640

Filename

6256640