Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Open Computer Science

Editor-in-Chief: van den Broek, Egon

Covered by:
Web of Science - Emerging Sources Citation Index

CiteScore 2018: 0.63
Source Normalized Impact per Paper (SNIP) 2018: 0.604

ICV 2017: 98.90

Open Access
See all formats and pricing
More options …

Clinical decision support system: risk level prediction of heart disease using weighted fuzzy rules and decision tree rules

Padmakumari Anooj
  • Department of Information Technology, Al Musanna College of Technology, Directorate General of Technological Education, Ministry of Manpower, Muscat, Sultanate of Oman
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2011-12-27 | DOI: https://doi.org/10.2478/s13537-011-0032-y


The development of medical domain applications has been one of the most active research areas recently. One example of a medical domain application is a detection system for heart disease based on computer-aided diagnosis methods, where the data is obtained from some other sources and is evaluated by computer based applications. Up to now, computers have usually been used to build knowledge based clinical decision support systems which used the knowledge from medical experts, and transferring this knowledge into computer algorithms was done manually. This process is time consuming and really depends on the medical expert’s opinion, which may be subjective. To handle this problem, machine learning techniques have been developed to gain knowledge automatically from examples or raw data. Here, a weighted fuzzy rule-based clinical decision support system (CDSS) is presented for the diagnosis of heart disease, automatically obtaining the knowledge from the patient’s clinical data. The proposed clinical decision support system for risk prediction of heart patients consists of two phases, (1) automated approach for generation of weighted fuzzy rules and decision tree rules, and, (2) developing a fuzzy rule-based decision support system. In the first phase, we have used the mining technique, attribute selection and attribute weightage method to obtain the weighted fuzzy rules. Then, the fuzzy system is constructed in accordance with the weighted fuzzy rules and chosen attributes. Finally, the experimentation is carried out on the proposed system using the datasets obtained from the UCI repository and the performance of the system is compared with the neural network-based system utilizing accuracy, sensitivity and specificity.

Keywords: clinical decision support system (CDSS); heart disease; fuzzy logic; weighted fuzzy rules; decision tree; attribute selection; risk prediction; UCI repository; accuracy; sensitivity and specificity

  • [1] Merijohn G.K., Bader J.D., Frantsve-Hawley J., Aravamudhan K., Clinical decision support chairside tools for evidence-based dental practice, Journal of Evidence-Based Dental Practice, 8(3), 119–132, 2008 http://dx.doi.org/10.1016/j.jebdp.2008.05.016CrossrefGoogle Scholar

  • [2] Subbalakshmi G., Ramesh K., Chinna Rao M., Decision support in heart disease prediction system using naive bayes, Indian Journal of Computer Science and Engineering (IJCSE), 2(2), 170–176, 2011 Google Scholar

  • [3] Parthiban L., Subramanian R., Intelligent heart disease prediction system using CANFIS and genetic algorithm, International Journal of Biological and Medical Sciences, 3(3), 108–115, 2008 Google Scholar

  • [4] Thuraisingham B., A Primer for understanding and applying data mining, IT Professional — IEEE Computer Society, 2(1), 28–31, 2000 http://dx.doi.org/10.1109/6294.819936CrossrefGoogle Scholar

  • [5] Tang T.I, Zheng G., Huang Y., Shu G., Wang P., A Comparative study of medical data classification methods based on decision tree and system reconstruction analysis, Industrial Engineering & Management Systems (IEMS), 4(1), 102–108, 2005 Google Scholar

  • [6] Garg A.X., Adhikari N.K., McDonald H., Rosas-Arellano M.P, Devereaux P.J, Beyene J., Sam J., Haynes R.B., Effects of computerized clinical decision support systems on practitioner performance and patient outcomes, J. Amer. Med. Assoc., 293(10), 1223–1238, 2005 http://dx.doi.org/10.1001/jama.293.10.1223CrossrefGoogle Scholar

  • [7] Miller R.A., Why the standard view is standard: people, not machines, understand patients problems, J. Med. Philos., 15(6), 581–591, 1990 CrossrefGoogle Scholar

  • [8] Miller R.A., Masarie F.E., The demise of the Greek Oracle model for medical diagnostic systems, Method. Inform. Med., 29(1), 1–2, 1990 Google Scholar

  • [9] Osheroff J.A., Improving medication use and outcomes with clinical decision support: a step-by-step guide, The Healthcare Information and Management Systems Society, Chicago, IL, USA, 2009 Google Scholar

  • [10] Warren J., Beliakov G., Zwaag B., Fuzzy logic in clinical practice decision support system, In: Proceedings of the 33rd Hawaii International Conference on System Sciences, Maui, Hawaii, 1–10, 2000 Google Scholar

  • [11] Anderson J., Clearing the way for physicians’ use of clinical Information systems, Commun. ACM, 40(8), 83–90, 1997 http://dx.doi.org/10.1145/257874.257895CrossrefGoogle Scholar

  • [12] Abbasi M.M., Kashiyarndi S., Clinical Decision Support Systems: a discussion on different methodologies used in Health Care, 2006 Google Scholar

  • [13] Khatibi V., Montazer G.A., A fuzzy-evidential hybrid inference engine for coronary heart disease risk assessment, Expert Sys. Appl., 37(12), 8536–8542, 2010 http://dx.doi.org/10.1016/j.eswa.2010.05.022CrossrefGoogle Scholar

  • [14] Tsipouras M.G., Exarchos T.P., Fotiadis D.I., Kotsia A.P., Vakalis K.V., Naka K.K., Michalis L.K., Automated diagnosis of coronary artery disease based on data mining and fuzzy modeling, IEEE T. Inf. Technol. B., 12(4), 447–458, 2008 http://dx.doi.org/10.1109/TITB.2007.907985Web of ScienceCrossrefGoogle Scholar

  • [15] Setiawan N.A., Venkatachalam P.A., Hani A.F.M., Diagnosis of coronary artery disease using artificial intelligence based decision support system, In: Proceedings of the International Conference on Man-Machine Systems, Batu Ferringhi, Penang, 1–5, 2009 Google Scholar

  • [16] Jilani T.A., Yasin H., Yasin M., Ardil C., Acute coronary syndrome prediction using data mining techniques-an application, World Academy of Science, Engineering and Technology, 59(4), 295–299, 2009 Google Scholar

  • [17] Patil S.B., Kumaraswamy Y.S., Intelligent and effective heart attack prediction system using data mining and artificial neural network, European Journal of Scientific Research, 31(4), 642–656, 2009 Google Scholar

  • [18] Palaniappan S., Awang R., Intelligent heart disease prediction system using data mining techniques, International Journal of Computer Science and Network Security, 8(8), 108–115, 2008 Google Scholar

  • [19] Fidele B., Cheeneebash J., Gopaul A., Goorah S.S.D., Artificial neural network as a clinical decision-supporting tool to predict cardiovascular disease, Trends in Applied Sciences Research, 4(1), 36–46, 2009 http://dx.doi.org/10.3923/tasr.2009.36.46CrossrefGoogle Scholar

  • [20] Abidin B., Dom R.M., Rahman A.R.A., Bakar R.A., Use of fuzzy neural network to predict coronary heart disease in a Malaysian sample, In: Proceedings of the 8th WSEAS International Conference on Telecommunications and Informatics, Istanbul, Turkey, 76–80, 2009 Google Scholar

  • [21] Shanthi D., Sahoo G., Saravanan N., Input feature selection using hybrid neuro-genetic approach in the diagnosis of stroke disease, International Journal of Computer Science and Network Security, 8(12), 2008 Google Scholar

  • [22] Yan H., Zheng J., Jiang Y., Peng C., Li Q., Development of a decision support system for heart disease diagnosis using multilayer perceptron, In: Proceedings of the 2003 International Symposium on Circuits and Systems, 5(1), 709–712, 2003 Google Scholar

  • [23] Lin L., Hu P.J.H., Sheng O.R.L., Decision support systems, Decis. Support. Syst., 42(2), 1152–1169, 2006 http://dx.doi.org/10.1016/j.dss.2005.10.007CrossrefGoogle Scholar

  • [24] Musen M., Shahar Y., Shortliffe E., Clinical decision-support systems, Biomedical Informatics, 2, 698–736, 2006 http://dx.doi.org/10.1007/0-387-36278-9_20CrossrefGoogle Scholar

  • [25] Szolovits P., Uncertainty and decision in medical informatics, Method. Inform. Med., 34(2), 111–121, 1995 Google Scholar

  • [26] G. Kong, Xu D.L., Yang J.B., Clinical decision support systems: a review on knowledge representation and inference under uncertainties, Int. J. Comput. Int. Sys., 1(2), 159–167, 2008 Google Scholar

  • [27] Ali S., Chia P., Ong K., Graphical knowledge-based protocols for chest pain management, computer in cardiology, IEEE, 309–312 1999 Google Scholar

  • [28] Straszecka E., Combining uncertainty and imprecision in models of medical diagnosis, Inform. Sciences, 176(20), 3026–3059, 2007 http://dx.doi.org/10.1016/j.ins.2005.12.006CrossrefGoogle Scholar

  • [29] De S.K., Biswas A., Roy R., An application of intuitionistic fuzzy sets in medical diagnosis, Fuzzy. Set. Syst., 117(2), 209–213, 2001 http://dx.doi.org/10.1016/S0165-0114(98)00235-8CrossrefGoogle Scholar

  • [30] Ephzibah E.P., Cost effective approach on feature selection using genetic algorithms and LS-SVM classifier, IJCA (International Journal of Computer Applications), 1, 16–20, 2010 Google Scholar

  • [31] Newman D.J., Hettich S., Blake C.L., Merz C.J., UCI Repository of machine learning databases, University California Irvine, Department of Information and Computer Science, Irvine, CA, 1998 Google Scholar

  • [32] Knopp R.H., Risk factors for coronary artery disease in women, Am. J. Cardiol., 89(12A), 28–34, 2002 http://dx.doi.org/10.1016/S0002-9149(02)02409-8CrossrefGoogle Scholar

  • [33] Bradley A.P., The use of the area under the roc curve in the evaluation of machine learning algorithms, Patteern Recogn., 30(7), 1145–1159, 1997 http://dx.doi.org/10.1016/S0031-3203(96)00142-2CrossrefGoogle Scholar

  • [34] Muennig P., Jia H. and Khan K., Hospitalization for heart attack, stroke, or congestive heart failure among persons with diabetes, special report: 2001–2003, New Mexico, BMC Cardiovascular Disorders, 4(1), 19, 2004 http://dx.doi.org/10.1186/1471-2261-4-19CrossrefGoogle Scholar

  • [35] Chen J., Greiner R., Comparing bayesian network classifiers, In: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, Stockholm, Sweden, 101–108, 1999 Google Scholar

  • [36] Han J., Pei J., Yin Y., Mao R., Mining frequent patterns without candidate generation: a frequent-pattern tree approach, Data Min. Knowl. Disc., 8(1), 53–87, 2004 http://dx.doi.org/10.1023/B:DAMI.0000005258.31418.83CrossrefGoogle Scholar

  • [37] Kawamoto K., Houlihan C.A., Balas E.A., Lobach D.F., Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success, Brit. Med. J., 330(7494), 765, 2005 http://dx.doi.org/10.1136/bmj.38398.500764.8FCrossrefGoogle Scholar

  • [38] Zadeh, L.A., Fuzzy sets, Inform. Control., 8(3), 338–353, 1965 CrossrefGoogle Scholar

  • [39] Agrawal R., Imielinski T., Swami A., Mining association rules between sets of items in large databases, In: Proceedings of 1993 ACM SIGMOD Int. Conf. on Management of Data, Washington, DC, USA, 207–216, 1993 Google Scholar

  • [40] Zhu W., Zeng N., Wang N., Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with Practical SAS®Implementations, In: NESUG proceedings: Health Care and Life Sciences, Baltimore, Maryland, USA, 2010 Google Scholar

  • [41] Quinlan J.R., Induction of decision trees, Mach. Learn., 1(1), 81–106, 1986 Google Scholar

About the article

Published Online: 2011-12-27

Published in Print: 2011-12-01

Citation Information: Open Computer Science, Volume 1, Issue 4, Pages 482–498, ISSN (Online) 2299-1093, DOI: https://doi.org/10.2478/s13537-011-0032-y.

Export Citation

© 2011 Versita Warsaw. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

Roohallah Alizadehsani, Moloud Abdar, Mohamad Roshanzamir, Abbas Khosravi, Parham M. Kebria, Fahime Khozeimeh, Saeid Nahavandi, Nizal Sarrafzadegan, and U. Rajendra Acharya
Computers in Biology and Medicine, 2019, Volume 111, Page 103346
Roohallah Alizadehsani, Mohammad Javad Hosseini, Abbas Khosravi, Fahime Khozeimeh, Mohamad Roshanzamir, Nizal Sarrafzadegan, and Saeid Nahavandi
Computer Methods and Programs in Biomedicine, 2018

Comments (0)

Please log in or register to comment.
Log in