Improve the Performance of K-means by using Genetic Algorithm for Classification Heart Attack

Abstract

In this research the k-means method was used for classification purposes after it was improved using genetic algorithms. An automated classification system for heart attack was implemented based on the intelligent recruitment of computer capabilities at the same time characterized by high performance based on (270) real cases stored within a globally database known (Statlog). The proposed system aims to support the efforts of staff in medical felid to reduce the diagnostic errors committed by doctors who do not have sufficient experience or because of the fatigue that the doctor suffers as a result of work pressure. The proposed system goes through two stages: in the first-stage genetic algorithm is used to select important features that have a strong influence in the classification process. These features forms the inputs to the K-means method in the second-stage which uses the selected features to divide the database into two groups one of them contain cases infected with the disease while the other group contains the correct cases depending on the distance Euclidean. The comparison of performance for the method (K-means) before and after addition genetic algorithm shows that the accuracy of the classification improves remarkably where the accuracy of classification was raised from (68..1481) in the case of use (k- means only) to (84.741) when improved the method by using genetic algorithm.