Fuzzy Decision Support System for Coronary Artery Disease Diagnosis Based on Rough Set Theory

Fuzzy Decision Support System for Coronary Artery Disease Diagnosis Based on Rough Set Theory

Noor Akhmad Setiawan (Universitas Gadjah Mada, Indonesia)
Copyright: © 2017 |Pages: 18
DOI: 10.4018/978-1-5225-1908-9.ch055
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The objective of this research is to develop an evidence based fuzzy decision support system for the diagnosis of coronary artery disease. The development of decision support system is implemented based on three processing stages: rule generation, rule selection and rule fuzzification. Rough Set Theory (RST) is used to generate the classification rules from training data set. The training data are obtained from University California Irvine (UCI) data repository. Rule selection is conducted by transforming the rules into a decision table based on unseen data set. Furthermore, RST attributes reduction is proposed and applied to select the most important rules. The selected rules are transformed into fuzzy rules based on discretization cuts of numerical input attributes and simple triangular and trapezoidal membership functions. Fuzzy rules weighing is also proposed and applied based on rules support on the training data. The system is validated using UCI heart disease data sets collected from the U.S., Switzerland and Hungary and data set from Ipoh Specialist Hospital Malaysia. The system is verified by three cardiologists. The results show that the system is able to give the approximate possibility of coronary artery blocking.
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AI and Knowledge Discovery from Data (KDD) methods have been used as a decision support tool to diagnose CAD (Setiawan et al., 2011). The AI and KDD methods use heterogeneous data from the patients such as physical and historical data, exercise tests, and laboratory tests to develop a computer system that can classify and diagnose the presence of CAD.

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