title: A data mining approach for diagnosis of coronary artery disease.
abstract: 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.
authors.affiliation: Software Engineering, Department of Computer Engineering, Sharif University of Technology, Azadi Avenue, Tehran, Iran.
authors.forename: Roohallah
authors.initials: R
authors.lastname: Alizadehsani
authors.forename: Jafar
authors.initials: J
authors.lastname: Habibi
authors.forename: Mohammad Javad
authors.initials: MJ
authors.lastname: Hosseini
authors.forename: Hoda
authors.initials: H
authors.lastname: Mashayekhi
authors.forename: Reihane
authors.initials: R
authors.lastname: Boghrati
authors.forename: Asma
authors.initials: A
authors.lastname: Ghandeharioun
authors.forename: Behdad
authors.initials: B
authors.lastname: Bahadorian
authors.forename: Zahra Alizadeh
authors.initials: ZA
authors.lastname: Sani
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001499:Bayes Theorem; D003324:Coronary Artery Disease / Q000175:diagnosis*; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual / Q000706:statistics & numerical data; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer