TY - JOUR
T1 - A data mining approach for diagnosis of coronary artery disease
AU - Alizadehsani, Roohallah
AU - Habibi, Jafar
AU - Hosseini, Mohammad Javad
AU - Mashayekhi, Hoda
AU - Boghrati, Reihane
AU - Ghandeharioun, Asma
AU - Bahadorian, Behdad
AU - Sani, Zahra Alizadeh
PY - 2013/7
Y1 - 2013/7
N2 - Cardiovascular diseases are very common and are one of the main reasons of death. Being among the major types of these diseases, correct and in-time diagnosis of coronary artery disease (CAD) is very important. Angiography is the most accurate CAD diagnosis method; however, it has many side effects and is costly. Existing studies have used several features in collecting data from patients, while applying different data mining algorithms to achieve methods with high accuracy and less side effects and costs. In this paper, a dataset called Z-Alizadeh Sani with 303 patients and 54 features, is introduced which utilizes several effective features. Also, a feature creation method is proposed to enrich the dataset. Then Information Gain and confidence were used to determine the effectiveness of features on CAD. Typical Chest Pain, Region RWMA2, and age were the most effective ones besides the created features by means of Information Gain. Moreover Q Wave and ST Elevation had the highest confidence. Using data mining methods and the feature creation algorithm, 94.08% accuracy is achieved, which is higher than the known approaches in the literature.
AB - Cardiovascular diseases are very common and are one of the main reasons of death. Being among the major types of these diseases, correct and in-time diagnosis of coronary artery disease (CAD) is very important. Angiography is the most accurate CAD diagnosis method; however, it has many side effects and is costly. Existing studies have used several features in collecting data from patients, while applying different data mining algorithms to achieve methods with high accuracy and less side effects and costs. In this paper, a dataset called Z-Alizadeh Sani with 303 patients and 54 features, is introduced which utilizes several effective features. Also, a feature creation method is proposed to enrich the dataset. Then Information Gain and confidence were used to determine the effectiveness of features on CAD. Typical Chest Pain, Region RWMA2, and age were the most effective ones besides the created features by means of Information Gain. Moreover Q Wave and ST Elevation had the highest confidence. Using data mining methods and the feature creation algorithm, 94.08% accuracy is achieved, which is higher than the known approaches in the literature.
KW - Bagging
KW - Classification
KW - Coronary artery disease
KW - Data mining
KW - Neural Networks
KW - SMO
UR - http://www.scopus.com/inward/record.url?scp=84878521202&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84878521202&partnerID=8YFLogxK
U2 - 10.1016/j.cmpb.2013.03.004
DO - 10.1016/j.cmpb.2013.03.004
M3 - Article
C2 - 23537611
AN - SCOPUS:84878521202
SN - 0169-2607
VL - 111
SP - 52
EP - 61
JO - Computer Methods and Programs in Biomedicine
JF - Computer Methods and Programs in Biomedicine
IS - 1
ER -