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- Type of Document: M.Sc. Thesis
- Language: Farsi
- Document No: 43249 (19)
- University: Sharif University of Technology
- Department: Computer Engineering
- Advisor(s): Habibi, Jafar
- Abstract:
- Cardiovascular diseases are very common nowadays 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. The best and most accurate CAD diagnosis method by now is recognized as Angiography, which has many side effects and is costly. Thus researchers are seeking for inexpensive, though still accurate, methods. Existing studies have used several features in collecting data from patients, while applying different data mining algorithms to increase accuracy. In this thesis, a data set is introduced which utilizes several new and effective features for CAD diagnosis, as well as a number of important and previously known ones. Also, a feature creation method is proposed in order to effectively increase the accuracy. The data set used in this thesis is gathered from 303 random visitors to Tehran’s Shaheed Rajaei Cardiovascular, Medical and Research Center, who had been suspicions of having CAD. Among the samples, 87 are healthy and 216 have CAD. Several data mining methods have been applied on the data set and a maximum 94.08% accuracy is achieved in diagnosing CAD, which is higher than the known approaches in the literature
- Keywords:
- Classification ; Data Mining ; Bagging ; Heart Diseases ; Coronary Arteries Disease (CAD) ; Sequential Minimal Optimization (SMO)
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