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.

1. INTRODUCTION
The tremendous progress that has accompanied computer science and the success it has achieved in various applications has made it more than just a computing machine and this has been a powerful motivation for scientists to develop and invent several technologies that try to exploit the capabilities of the computer to accomplish useful functions and find solutions to many problems to facilitate the joints of human life and reduce the problems that may be faced so many techniques have emerged including: (expert systems, networks and classification algorithms of various types) [3] .
Classification of diseases is a distinctive goal of artificial intelligence research that has tried to support the medical field and provide specialists of doctors, centers and hospitals with diagnostic systems that help to improve the accuracy of decision made on a situation and reduce errors that may be made in the diagnosis because of lack of experience or pressure stress which leads to problems in the accuracy of the diagnosis for specialist and also provides detailed medical data about the test in record time [6-8] .
The heart attack is one of the dangers diseases that threaten human life where The World Health Organization (WHO) reports that 12 million people die each year from heart disease [1]. Because the severity of disease many computer specialists presented on many years a lot of research aimed to supporting medical institutions and their staff with systems to diagnose this disease and research is still ongoing in the field [5].
Researchers rely on a global database known as )Statlog(. This database used in research that work on classification heart attack to measure the strength of the method proposed by the research. It can be obtained from the data warehouse ) UCI( allocated each row in this database for each patient. The total number of cases (patients) in the database are (270) case and each person stored 13 information (property): (age), (sex), (chest pain type), (blood pressure), (cholesterol), (blood sugar), (electrocardiographic results), (maximum heart rate) and other properties. The property 14 is represent the final diagnosis: the value of this property is (1) to indicate for infected person while the healthy person referred by making the value of property 14 equal to (0).