Paper Info
Title
Emerging Patterns Based Methodology for Prediction of Patients with Myocardial Ischemia
Abstract
Heart disease is the one of the significant health problem in the world. Recently, most serious problem caused by it is that the patient becomes younger. Therefore, it is very important and necessary to find the early symptoms of heart problems for better treatment and effective methodology for predicting the disease. Data mining is the one of the efficient approaches. However, there are still some tasks have to be solved. One is that the result should make it easy to explain the relationship between class label and predictors for the heart disease data. In this paper, redefined T-tree algorithm is used to mine the emerging patterns to perform the work and solve the problem. Also, the aggregate score is considered to build classifier for the prediction work. The algorithms CMAR, CPAR, C4.5 and our method are applied to the dataset and the proposed method shows the better accuracy than others (The accuracy is between 75% to 85%).
Year
DOI
Venue
2009
10.1109/FSKD.2009.638
FSKD (1)
Keywords
Field
DocType
prediction work,heart disease,diseases,trees (mathematics),disease prediction,prediction,emerging pattern,myocardial ischemia,heart disease data,emerging patterns,better treatment,ischemia,patient prediction,serious problem,t-tree,aggregation score,data mining,better accuracy,medical computing,t-tree algorithm,significant health problem,heart problem,heart,classification algorithms,accuracy,t tree,prediction algorithms
Data mining,Disease,Computer science,T-tree,Prediction algorithms,Artificial intelligence,Classifier (linguistics),Statistical classification,Machine learning,Heart disease
Conference
Volume
ISBN
Citations 
1
978-0-7695-3735-1
2
PageRank 
References 
Authors
0.35
12
5
Name
Order
Citations
PageRank
Minghao Piao1376.30
Heon Gyu Lee2727.77
Gyo Yong Sohn340.71
Gouchol Pok412414.72
Keun Ho Ryu588385.61