A data mining approach for diagnosis of coronary artery disease

• Published in: Computer methods and programs in biomedicine Vol. 111; no. 1; pp. 52 - 61
• Main Authors: Alizadehsani, Roohallah, Habibi, Jafar, Hosseini, Mohammad Javad, Mashayekhi, Hoda, Boghrati, Reihane, Ghandeharioun, Asma, Bahadorian, Behdad, Sani, Zahra Alizadeh
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ireland Ltd 01.07.2013; Elsevier
• Subjects: Adult; Aged; Aged, 80 and over; Algorithms; Applied sciences; Bagging; Bayes Theorem; Biological and medical sciences; Cardiovascular system; Classification; Computer science; control theory; systems; Coronary artery disease; Coronary Artery Disease - diagnosis; Data mining; Data Mining - methods; Data processing. List processing. Character string processing; Databases, Factual - statistics & numerical data; Diagnosis, Computer-Assisted - methods; Exact sciences and technology; Female; Humans; Internal Medicine; Investigative techniques, diagnostic techniques (general aspects); Male; Medical sciences; Memory organisation. Data processing; Middle Aged; Neural Networks; Neural Networks (Computer); Other; Radionuclide investigations; SMO; Software; Neural Networks; SMO; Data mining; Coronary artery disease; Bagging; Classification; Data analysis; Angiography; Coronary artery; High precision; Cardiovascular disease; Neural network; Aggregate model; Confidence; Edge effect; Efficiency; Medical imagery; Diagnosis; Age; Computer aided design; Data gathering
• ISSN: 0169-2607; 1872-7565; 1872-7565
• DOI: 10.1016/j.cmpb.2013.03.004

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.