A data mining approach for diagnosis of coronary artery disease

Alizadehsani, R. and Habibi, J. and Hosseini, M.J. and Mashayekhi, H. and Boghrati, R. and Ghandeharioun, A. and Bahadorian, B. and Sani, Z.A. (2013) A data mining approach for diagnosis of coronary artery disease. Computer Methods and Programs in Biomedicine, 111 (1). pp. 52-61.

Full text not available from this repository.
Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....


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. © 2013 Elsevier Ireland Ltd.

Item Type: Article
Additional Information: cited By 57
Uncontrolled Keywords: Bagging; Cardio-vascular disease; Coronary artery disease; Data mining algorithm; Data mining methods; Diagnosis methods; Information gain; SMO, Algorithms; Classification (of information); Data mining; Diseases; Neural networks, Diagnosis, age; algorithm; article; controlled study; coronary artery disease; data mining; diagnostic accuracy; Q wave; sequential minimal optimization; ST segment elevation; thorax pain; Z Alizadeh Sani dataset, Adult; Aged; Aged, 80 and over; Algorithms; Bayes Theorem; Coronary Artery Disease; Data Mining; Databases, Factual; Diagnosis, Computer-Assisted; Female; Humans; Male; Middle Aged; Neural Networks (Computer)
Subjects: WG Cardiovascular System
Depositing User: somayeh pourmorteza
Date Deposited: 19 May 2019 06:38
Last Modified: 19 May 2019 06:38
URI: http://eprints.iums.ac.ir/id/eprint/9672

Actions (login required)

View Item View Item