Title :
Using chaotic adaptive PSO-SVM for heart disease diagnosis
Author :
Yang, Haiyan ; Luo, Qifang ; Zhou, Yongquan
Author_Institution :
Coll. of Math. & Comput. Sci., Guangxi Univ. for Nat., Nanning, China
Abstract :
In this paper, the application of support vector machine (SVM) approach based on the statistics-learning theory of structural risk minimization in heart disease diagnosis. Aiming at the blindness of man made choice of parameter and kernel function of SVM, a chaotic adaptive particle swarm optimization (CAPSO) method is applied to select parameters of SVM in the paper and genetic characteristics of the subset of choices (GA_FSS) is applied to reduce large dimensions and improve greatly the accuracy of classification. The experimental results on heart disease diagnosis problem show that CAPSO-SVM . classifier algorithm is effective and correct.
Keywords :
genetic algorithms; medical computing; particle swarm optimisation; support vector machines; chaotic adaptive PSO-SVM; genetic characteristics; heart disease diagnosis; kernel function; parameter function; particle swarm optimization; statistics-learning theory; structural risk minimization; support vector machine; Cardiac disease; Chaos; Educational institutions; Genetics; Mathematics; Neural networks; Particle swarm optimization; Statistical learning; Support vector machine classification; Support vector machines; Chaos optimization; Feature subset selection (FSS); Genetic algorithm (GA); Support vector machine (SVM); Swarm optimization algorithm;
Conference_Titel :
Computer Science and Information Technology, 2009. ICCSIT 2009. 2nd IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-4519-6
Electronic_ISBN :
978-1-4244-4520-2
DOI :
10.1109/ICCSIT.2009.5234450
