ارائه یک سیستم خبره فازی برای مدلسازی تشخیص میزان ابتلای به بیماری قلبی وعروقی کرونر

نوع مقاله: مقاله پژوهشی

نویسندگان

1 استادیار گروه مدیریت عملیات و فناوری اطلاعات دانشگاه خوارزمی (نویسنده مسئول)

2 متخصص پزشکی هسته ای، مرکز پزشکی هسته ای یاس

3 کارشناس ارشد مدیریت فناوری اطلاعات دانشگاه خوارزمی

چکیده

بیماری های قلبی عروقی، همه ساله میلیون ها نفر را در جهان مبتلا نموده و بخش اعظمی از بیماری ها را به خود
اختصاص داده است. اگرچه پیشرفت های بسیاری در حوزه دانش پزشکی  صورت گرفته ، ولی تشخیص زود هنگام بیماری، همچنان یک چالش، برای پیشگیری از ابتلای به آن هست. هدف این پژوهش، طراحی وایجاد یک سیستم خبره تشخیص است که به تشخیص بیماری قلبی و عروقی کرونر، در مراحل اولیه  کمک
کند. قواعد با کمک پزشکان ایجاد شدند و روش استنتاج فازی، برای فائق آمدن بر عدم اطمینان موجود بکار 
گرفته شد. نتایج حاصل از سیستم طراحی شده، بیانگر میزان بالای تشخیص درست گروه ها ی نرمال و غیر
نرمال از افراد هست. سیستم طراحی شده می تواند به ارائه خدمات به افرادی بپردازد که در حوزه دانش
پزشکی  فعالیت می کنند و به پیش بینی وضعیت خطر بیماران کمک کند.

کلیدواژه‌ها


عنوان مقاله [English]

A Fuzzy Expert System for the Diagnosis of Coronary Artery Disease

نویسندگان [English]

  • Reza Yousefi Zenouz 1
  • Reza Olamaie 2
  • Somayeh Olamaie 3
چکیده [English]

Millions of people encounter coronary artery disease annually, and this disease constitutes a large number of patients. Although considerable progress has been made in medical science, but the early detection of this disease is still a challenging issue. The aim of this paper is to describe developing a fuzzy expert system that will help to detect CAD at an early stage. Rules were extracted with the aid of doctors and fuzzy approach was taken to cope with the present uncertainty in the medical domain. The results indicate a high level of correct detection of normal and abnormal groups of people. The suggested methodology can help the doctors in medical diagnosis

کلیدواژه‌ها [English]

  • Coronary Artery Disease
  • Fuzzy Expert System
  • Fuzzy Rules
  • Fuzzy Logic
 
 
منابع
  • Abrishami, Z., & Tabatabaee, H. (2015). Design of a fuzzy expert system and a multi-layer neural network system for diagnosis of hypertension. Bull. Env. Pharmacol. Life Sci, 4, 138-145.
  • Adeli, A., & Neshat, M. (2010, March). A fuzzy expert system for heart disease diagnosis. In Proceedings of International Multi Conference of Engineers and Computer Scientists, Hong Kong (Vol. 1).
  • Adlassnig, K., & Kolarz, G. (1982). Computer-assisted medical diagnosis using fuzzy subsets. Approximate Reasoning in Decision Analysis (North-Holland, Amsterdam, 1982), 219-248.
  • Andreeva, P. (2006). Data modelling and specific rule generation via data mining techniques. In International Conference on Computer Systems and Technologies-CompSysTech.
  • Azadeh, A., Fam, I. M., Khoshnoud, M., & Nikafrouz, M. (2008). Design and implementation of a fuzzy expert system for performance assessment of an integrated health, safety, environment (HSE) and ergonomics system: The case of a gas refinery. Information Sciences, 178(22), 4280-4300.
  • Azar, A. and Faraji, H. (2008). Fuzzy Management Science, Ketab-e-Mehraban Publication(2nd), Tehran, Iran
  • Chirinos, J. A., Veerani, A., Zambrano, J. P., Schob, A., Perez, G., Mendez, A. J., & Chakko, S. (2007). Evaluation of comorbidity scores to predict all-cause mortality in patients with established coronary artery disease. International journal of cardiology, 117(1), 97-102.
  • Das, R., Turkoglu, I., & Sengur, A. (2009). Effective diagnosis of heart disease through neural networks ensembles. Expert systems with applications, 36(4), 7675-7680.
  • Durkin J. (1994). expert system design and development,Newyork Prentic Hall. : Macmillan Publishing Company, Inc., 1994
  • Esfahanipour, A., & Aghamiri, W. (2010). Adapted neuro-fuzzy inference system on indirect approach TSK fuzzy rule base for stock market analysis. Expert Systems with Applications, 37(7), 4742-4748.
  • Fasanghari, M., & Montazer, G. A. (2010). Design and implementation of fuzzy expert system for Tehran Stock Exchange portfolio recommendation. Expert Systems with Applications, 37(9), 6138-6147.
  • Gamberger, D., Lavrač, N., & Krstačić, G. (2003). Active subgroup mining: a case study in coronary heart disease risk group detection. Artificial Intelligence in Medicine, 28(1), 27-57.
  • Karimian, F. (2007). “Using fuzzy expert systems in ordinary buildings structure design.” Master Thesis, Architecture Faculty, University of Tehran, Iran.
  • Keleş, A., & Keleş, A. (2008). ESTDD: Expert system for thyroid diseases diagnosis. Expert Systems with Applications, 34(1), 242-246.
  • Korenevskiy, N. A. (2015). Application of fuzzy logic for decision-making in medical expert systems. Biomedical Engineering, 49(1), 46-49.
  • Kurt, I., Ture, M., & Kurum, A. T. (2008). Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease. Expert systems with applications, 34(1), 366-374.
  • Langarizade M, Khajehpour E, Khajehpour H, Noori T. (2014). Fuzzy Expert System for Diagnosis of Bacterial Meningitis from Other Types of Meningitis in Children. Journal of Health and Biomedical Informatics.; 1 (1) :19-25
  • Li, S., & Li, J. Z. (2010). AgentsInternational: Integration of multiple agents, simulation, knowledge bases and fuzzy logic for international marketing decision making. Expert Systems with Applications, 37(3), 2580-2587.
  • Maria Jose´ de, Paula Castanho La,e´cio, Carvalho de Barros Akebo Yamakami c, Lae´rcio Luis Vendite. (2008). An example in prostate cancer, elsevier, Applied Mathematics and Computation, (202) 78–85.
  • Maihami, V., Khormehr, A., & Rahimi, E. (2016). Designing an expert system for prediction of heart attack using fuzzy systems. Scientific Journal of Kurdistan University of Medical Sciences, 21(4), 118-131.
  • Muthukaruppan, S., & Er, M. J. (2012). A hybrid particle swarm optimization based fuzzy expert system for the diagnosis of coronary artery disease. Expert Systems with Applications, 39(14), 11657-11665.
  • Nalavade, J., Gavali, M., Gohil, N., & Jamale, S. (2014). Impelling Heart Attack Prediction System using Data Mining and Artificial Neural Network. International Journal of Current Engineering and Technology, 4(3), 1-5.
  • Ngai, E. W. T., & Wat, F. K. T. (2003). Design and development of a fuzzy expert system for hotel selection. Omega, 31(4), 275-286.
  • Omid, M., Lashgari, M., Mobli, H., Alimardani, R., Mohtasebi, S., & Hesamifard, R. (2010). Design of fuzzy logic control system incorporating human expert knowledge for combine harvester. Expert Systems with Applications, 37(10), 7080-7085
  • Omran, A. R. (1979). Changing patterns of health and disease during the process of national development. Health, Illness and Medicine: A Reader in Medical Sociology. Chicago, IL: Rand McNally.
  • Pal, D., Mandana, K. M., Pal, S., Sarkar, D., & Chakraborty, C. (2012). Fuzzy expert system approach for coronary artery disease screening using clinical parameters. Knowledge-Based Systems, 36, 162-174.
  • Russell, S. J., & Norvig, P. (1995). A modern, agent-oriented approach to introductory artificial intelligence. SIGART Bulletin, 6(2), 24-26.
  • Samavat T & Shams M. (2013).Prevention ways and Controling Coronary Artery Disease (especially for government staff), Ministry of Health and Medical Education.
  • Shantakumar B. (2009), Intelligent and Effective Heart Attack Prediction System Using Data Mining and Artificial Neural Network, European journal of scientific research ,vol.3.1 no.4,  pp 642 – 656
  • Shortliffe, E. H. (1976). Computer-based medical consultation. MYCIN.
  • Siler, W., & Buckley, J. J. (2005). Fuzzy expert systems and fuzzy reasoning. John Wiley & Sons.
  • Sohrabi, B., Vanani, I. R., Tahmasebipur, K., & Fazli, S. (2012). An exploratory analysis of hotel selection factors: A comprehensive survey of Tehran hotels. International Journal of Hospitality Management, 31(1), 96-106.
  • Tsipouras, M. G., Exarchos, T. P., Fotiadis, D. I., Kotsia, A. P., Vakalis, K. V., Naka, K. K., & Michalis, L. K. (2008). Automated diagnosis of coronary artery disease based on data mining and fuzzy modeling. IEEE Transactions on Information Technology in Biomedicine, 12(4), 447-458.
  • Ustundag, A., Kılınç, M. S., & Cevikcan, E. (2010). Fuzzy rule-based system for the economic analysis of RFID investments. Expert systems with applications, 37(7), 5300-5306.
  • Vaisi-Raygani, A., Ghaneialvar, H., Rahimi, Z., Nomani, H., Saidi, M., Bahrehmand, F., ... & Pourmotabbed, T. (2010). The angiotensin converting enzyme D allele is an independent risk factor for early onset coronary artery disease. Clinical biochemistry, 43(15), 1189-1194.
  • Zarandi, M. F., & Ahmadpour, P. (2009). Fuzzy agent-based expert system for steel making process. Expert systems with applications, 36(5), 9539-9547.
  • Zarandi, M. F., Zolnoori, M., Moin, M., & Heidarnejad, H. (2010). A fuzzy rule-based expert system for diagnosing asthma. Scientia Iranica. Transaction E, Industrial Engineering, 17(2), 129.