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

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.