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A data mining approach for diagnosis of coronary artery disease

Version 2 2024-06-05, 05:52
Version 1 2019-07-03, 13:26
journal contribution
posted on 2024-06-05, 05:52 authored by R Alizadehsani, J Habibi, MJ Hosseini, H Mashayekhi, R Boghrati, A Ghandeharioun, B Bahadorian, ZA Sani
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.

History

Journal

Computer methods and programs in biomedicine

Volume

111

Pagination

52-61

Location

Amsterdam, The Netherlands

ISSN

0169-2607

eISSN

1872-7565

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2013, Elsevier Ireland Ltd.

Issue

1

Publisher

Elsevier