Early Detection of Clinical Parameters in Heart Disease by Improved Decision Tree Algorithm

In this paper, we propose a new pruning method which is a combination of pre-pruning and post-pruning, aiming on both classification accuracy and tree size. Based upon this method, we induce a decision tree. The experimental results are computed by using 18 benchmark datasets from UCI Machine Learning Repository. The results, when compared to benchmark algorithms, indicate that our new tree pruning method considerably reduces the tree size and increases the accuracy in general. We have also conducted a case study of heart disease dataset by using our improved algorithm. This study suggests that (Thal), type of defect in heart is the most important predictor for confirming the presence of heart disease. Number of major vessels colored by fluoroscopy (MV) and type of chest pain (Chest) as biomarkers of heart disease.