Résumé
An abnormal condition which troubles a living organism is called a disease. Nowadays, the most common problems thepeople are affected by are the heart problems. Several times, they lead to death in most cases due to the lack of correctdiagnosis. The volume of data has been increasing rapidly in the area of health care. Predicting the heart problems is verydifficult for the physicians. It is intractable to find the interesting patterns among enormous volumes of data. To find those,pattern recognition can be used, and to discover the hidden knowledge, data mining can be used. There have been a largenumber of medical data sets available in the market. Among all types of heart diseases, Cardio Vascular Disease is a type.So, many researchers carried out their works in heart disease dataset with 13 attributes, and 15 attributes with various datamining methods. In this study, ranking method was used in preprocessing a stage with total of 17 attributes for strengtheningthe rate of accuracy. The Zero R and J48 algorithms from NN and Multilayer Perceptron & decision tree were appliedrespectively on the dataset. The classifiers’ performance was analyzed by error rate and time complexity with accuracy. Inthis research, Multilayer perceptron classifier showed high accuracy results with 13 attributes. Out of these three classifiers,J48 classifier gave high accuracy, minimum error rate and less time while using 17 attributes. Hence, these approaches canbe very useful to the physicians to take decisions at the proper time. This research work was entirely carried out by WEKA(Waikato Environment Knowledge Analysis) data mining tool.