The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A data mining approach for diagnosis of coronary artery disease
Open accessAbstract
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.
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Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907
Cited by
Arabasadi, Z., Alizadehsani, R., Roshanzamir, M., Moosaei, H., & Yarifard, A. A. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.01.004 · Cited by 499
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Metadata
- Authors
- Alizadehsani, R., Habibi, J., Hosseini, M. J., Mashayekhi, H., Boghrati, R., Ghandeharioun, A., Bahadorian, B., & Sani, Z. A.
- Venue
- Computer Methods and Programs in Biomedicine
- Publisher
- Elsevier BV
- Published
- 2013-07
- Volume
- 111
- Issue
- 1
- Pages
- 52-61
- ISSN
- 0169-2607 (ISSN-L)
- OpenAlex
- W2020176002
- Cited by
- 299
- References
- 2
- FWCI
- 17.94
- Type
- journal article
- Language
- en
- Open access
- green
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- The paper
- Alizadehsani, R., Habibi, J., Hosseini, M. J., Mashayekhi, H., Boghrati, R., Ghandeharioun, A., Bahadorian, B., & Sani, Z. A. (2013). A data mining approach for diagnosis of coronary artery disease. Computer Methods and Programs in Biomedicine. https://doi.org/10.1016/j.cmpb.2013.03.004
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