{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,6,2]],"date-time":"2022-06-02T06:12:13Z","timestamp":1654150333938},"reference-count":50,"publisher":"IGI Global","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,1,1]]},"abstract":"<p>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.<\/p>","DOI":"10.4018\/ijrsda.2014010105","type":"journal-article","created":{"date-parts":[[2014,7,28]],"date-time":"2014-07-28T15:34:28Z","timestamp":1406561668000},"page":"65-80","source":"Crossref","is-referenced-by-count":11,"title":["Fuzzy Decision Support System for Coronary Artery Disease Diagnosis Based on Rough Set Theory"],"prefix":"10.4018","volume":"1","author":[{"given":"Noor Akhmad","family":"Setiawan","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Information Technology Universitas Gadjah Mada, Yogyakarta, Indonesia"}]}],"member":"2432","reference":[{"key":"ijrsda.2014010105-0","unstructured":"Adeli, A., & Neshat, M. (2010). A fuzzy expert system for heart disease diagnosis. In Proceedings of International Multi Conference of Engineers and Computer Scientists 2010 (Vol I). IAENG."},{"key":"ijrsda.2014010105-1","unstructured":"Agotnes, T. (1999). Filtering large propositional rule sets while retaining classifier performance. Unpublished MSc Thesis, Norwegian Universtiy of Science and Technology."},{"key":"ijrsda.2014010105-2","author":"A.Alwan","year":"2011","journal-title":"Global status report on noncommunicable diseases 2010"},{"key":"ijrsda.2014010105-3","doi-asserted-by":"publisher","DOI":"10.1007\/BFb0029452"},{"key":"ijrsda.2014010105-4","doi-asserted-by":"publisher","DOI":"10.1016\/0002-9149(89)90524-9"},{"issue":"2","key":"ijrsda.2014010105-5","first-page":"1","article-title":"A new methodology of extraction, optimization and application of crisp and fuzzy logical rules.","volume":"11","author":"W.Duch","year":"2000","journal-title":"IEEE Transactions on Neural Networks"},{"key":"ijrsda.2014010105-6","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-39949-6_20"},{"key":"ijrsda.2014010105-7","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(89)90046-5"},{"key":"ijrsda.2014010105-8","doi-asserted-by":"publisher","DOI":"10.1136\/hrt.2007.134890"},{"key":"ijrsda.2014010105-9","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.39261.471806.55"},{"key":"ijrsda.2014010105-10","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.39609.449676.25"},{"key":"ijrsda.2014010105-11","doi-asserted-by":"crossref","unstructured":"Jankowski, N., & Kadirkamanathan, N. (1997, October 1997). Statistical control of RBF-like networks for classification. In Proceedings of the 7th International Conference on Artificial Neural Networks, Lausanne, Switzerland.","DOI":"10.1007\/BFb0020185"},{"key":"ijrsda.2014010105-12","unstructured":"Jayanta, K. G., & Marco, V. (2000). Building a Bayesian network model of heart disease. In Proceedings of the 38th Annual on the Southeast Regional Conference, Clemson, South Carolina."},{"key":"ijrsda.2014010105-13","doi-asserted-by":"publisher","DOI":"10.1016\/S0022-0000(74)80044-9"},{"key":"ijrsda.2014010105-14","doi-asserted-by":"publisher","DOI":"10.1016\/S0933-3657(98)00051-7"},{"key":"ijrsda.2014010105-15","doi-asserted-by":"crossref","unstructured":"Kukar, M., Groselj, C., Kononenko, I., & Fettich, J. J. (1997). An application of machine learning in the diagnosis of ischaemic heart disease. In Proceedings of the 10th IEEE Symposium on Computer-Based Medical Systems (CBMS '97).","DOI":"10.1109\/CBMS.1997.596411"},{"key":"ijrsda.2014010105-16","doi-asserted-by":"publisher","DOI":"10.3923\/jai.2012.47.55"},{"key":"ijrsda.2014010105-17","unstructured":"Li, J. (2007). Rough set based rule evaluations and their applications. Unpublished PhD thesis, University of Waterloo, Waterloo, Ontario."},{"key":"ijrsda.2014010105-18","doi-asserted-by":"crossref","unstructured":"Li, J., & Cercone, N. (2005). A rough set based model to rank the importance of association rules. In D. \u015al\u0119zak, J. Yao, J. F. Peters, W. Ziarko & X. Hu (Eds.), Rough sets, fuzzy sets, data mining, and granular computing (pp. 109).","DOI":"10.1007\/11548706_12"},{"key":"ijrsda.2014010105-19","unstructured":"Li, J., & Cercone, N. (2005a). Discovering and ranking important rules. In Proceedings of the IEEE International Conference on Granular Computing 2005."},{"key":"ijrsda.2014010105-20","doi-asserted-by":"publisher","DOI":"10.1007\/11847465_8"},{"key":"ijrsda.2014010105-21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-71200-8_9"},{"key":"ijrsda.2014010105-22","doi-asserted-by":"crossref","unstructured":"Liping, A., & Lingyun, T. (2005). A rough neural expert system for medical diagnosis. In Proceedings of the International Conference on Services Systems and Services Management.","DOI":"10.1109\/ICSSSM.2005.1500173"},{"key":"ijrsda.2014010105-23","doi-asserted-by":"crossref","unstructured":"Maddouri, M., & Gammoudi, J. (2007). On semantic properties of interestingness measures for extracting rules from data. Adaptive and Natural Computing Algorithms, 148.","DOI":"10.1007\/978-3-540-71618-1_17"},{"key":"ijrsda.2014010105-24","doi-asserted-by":"publisher","DOI":"10.1016\/S0020-7373(75)80002-2"},{"issue":"14","key":"ijrsda.2014010105-25","doi-asserted-by":"crossref","first-page":"11657","DOI":"10.1016\/j.eswa.2012.04.036","article-title":"A hybrid particle swarm optimization based fuzzy expert system for the diagnosis of coronary artery disease.","volume":"39","author":"S.Muthukaruppan","year":"2013","journal-title":"Expert Systems with Applications"},{"key":"ijrsda.2014010105-26","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2012.07.032"},{"key":"ijrsda.2014010105-27","author":"D. J.Newman","year":"1998","journal-title":"UCI repository of machine learning databases: University California Irvine"},{"key":"ijrsda.2014010105-28","unstructured":"Ohrn, A. (1999). Discernibility and rough sets in medicine: Tools and applications. Unpublished PhD thesis, Norwegian University of Science and Technology, Trondheim."},{"key":"ijrsda.2014010105-29","doi-asserted-by":"publisher","DOI":"10.1007\/BF01001956"},{"key":"ijrsda.2014010105-30","doi-asserted-by":"crossref","unstructured":"Pedreira, C. E., Macrini, L., & Costa, E. S. (2005). Input and data selection applied to heart disease diagnosis. In Proceedings of the International Joint Conference on Neural Networks, Montreal, Canada.","DOI":"10.1109\/IJCNN.2005.1556276"},{"key":"ijrsda.2014010105-31","author":"B.Phibbs","year":"2007","journal-title":"The human heart: A basic guide to heart disease"},{"key":"ijrsda.2014010105-32","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2006.05.028"},{"key":"ijrsda.2014010105-33","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2006.01.027"},{"key":"ijrsda.2014010105-34","author":"O. S.Randall","year":"2005","journal-title":"The encyclopedia of the heart and heart disease"},{"key":"ijrsda.2014010105-35","doi-asserted-by":"crossref","DOI":"10.1525\/9780520334359","author":"A.Selzer","year":"1992","journal-title":"Understanding heart disease"},{"key":"ijrsda.2014010105-36","doi-asserted-by":"publisher","DOI":"10.1504\/IJMEI.2011.039077"},{"issue":"5","key":"ijrsda.2014010105-37","first-page":"198","article-title":"Rule selection for coronary artery disease diagnosis based on rough set.","volume":"2","author":"N. A.Setiawan","year":"2009","journal-title":"International Journal of Recent Trends in Engineering"},{"key":"ijrsda.2014010105-38","doi-asserted-by":"crossref","unstructured":"Setiawan, N. A., Venkatachalam, P. A., & Hani, A. F. M. (2007). Missing data estimation on heart disease using artificial neural network and rough set theory. In Proceedings of the International Conference on Intelligent and Advanced Systems, Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICIAS.2007.4658361"},{"key":"ijrsda.2014010105-39","unstructured":"Setiawan, N. A., Venkatachalam, P. A., & Hani, A. F. M. (2008a). A comparative study of imputation methods to predict missing attribute values in coronary heart disease data set. In Proceedings of the 4th Kuala Lumpur International Conference on Biomedical Engineering, Kuala Lumpur."},{"key":"ijrsda.2014010105-40","doi-asserted-by":"crossref","unstructured":"Setiawan, N. A., Venkatachalam, P. A., & Hani, A. F. M. (2008b). Missing attribute value prediction based on artificial neural network and rough set theory. In Proceedings of the International Conference on Biomedical Engineering and Informatics, Sanya, Hainan, China.","DOI":"10.1109\/BMEI.2008.322"},{"key":"ijrsda.2014010105-41","doi-asserted-by":"crossref","unstructured":"Tsipouras, M. G., Exarchos, T. P., Fotiadis, D. I., Kotsia, A., Naka, A., & Michalis, L. K. (2006). A decision support system for the diagnosis of coronary artery disease. In Proceedings of the 19th IEEE Symposium on Computer-Based Medical System.","DOI":"10.1109\/CBMS.2006.9"},{"key":"ijrsda.2014010105-42","doi-asserted-by":"publisher","DOI":"10.1109\/TITB.2007.907985"},{"key":"ijrsda.2014010105-43","doi-asserted-by":"publisher","DOI":"10.1109\/91.324806"},{"key":"ijrsda.2014010105-44","author":"P.Weissberg","year":"2012","journal-title":"Coronary heart disease statistics"},{"key":"ijrsda.2014010105-45","doi-asserted-by":"publisher","DOI":"10.1161\/01.CIR.97.18.1837"},{"key":"ijrsda.2014010105-46","author":"I. H.Witten","year":"2005","journal-title":"Data mining: Practical machine learning tools and techniques"},{"key":"ijrsda.2014010105-47","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2005.07.022"},{"key":"ijrsda.2014010105-48","doi-asserted-by":"publisher","DOI":"10.1016\/S0019-9958(65)90241-X"},{"key":"ijrsda.2014010105-49","author":"B. L.Zaret","year":"1992","journal-title":"Yale university school of medicine heart book"}],"container-title":["International Journal of Rough Sets and Data Analysis"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=111313","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,2]],"date-time":"2022-06-02T05:38:10Z","timestamp":1654148290000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/ijrsda.2014010105"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2014,1,1]]},"references-count":50,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2014,1]]}},"URL":"https:\/\/doi.org\/10.4018\/ijrsda.2014010105","relation":{},"ISSN":["2334-4598","2334-4601"],"issn-type":[{"value":"2334-4598","type":"print"},{"value":"2334-4601","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,1,1]]}}}