Feature Analysis of Coronary Artery Heart Disease Data Sets

Abstract

Data sets dealing with the same medical problems like Coronary artery disease (CAD) may show different results when applying the same machine learning technique. The classification accuracy results and the selected important features are based mainly on the efficiency of the medical diagnosis and analysis. The aim of this work is to apply an integration of the results of the machine learning analysis applied on different data sets targeting the CAD disease. This will avoid the missing, incorrect, and inconsistent data problems that may appear in the data collection. Fast decision tree and pruned C4.5 tree are applied where the resulted trees are extracted from different data sets and compared. Common features among these data sets are extracted and used in the later analysis for the same disease in any data set. The results show that the classification accuracy of the collected dataset is 78.06% higher than the average of the classification accuracy of all separate datasets which is 75.48%.