A Two-Stage Expert System for Diagnosis of Leukemia Based on Type-2 Fuzzy Logic
Authors: Ali Akbar Sadat Asl
Diagnosis and deciding about diseases in medical fields is facing innate uncertainty which can affect the whole process of treatment. This decision is made based on expert knowledge and the way in which an expert interprets the patient's condition, and the interpretation of the various experts from the patient's condition may be different. Fuzzy logic can provide mathematical modeling for many concepts, variables, and systems that are unclear and ambiguous and also it can provide a framework for reasoning, inference, control, and decision making in conditions of uncertainty. In systems with high uncertainty and high complexity, fuzzy logic is a suitable method for modeling. In this paper, we use type-2 fuzzy logic for uncertainty modeling that is in diagnosis of leukemia. The proposed system uses an indirect-direct approach and consists of two stages: In the first stage, the inference of blood test state is determined. In this step, we use an indirect approach where the rules are extracted automatically by implementing a clustering approach. In the second stage, signs of leukemia, duration of disease until its progress and the output of the first stage are combined and the final diagnosis of the system is obtained. In this stage, the system uses a direct approach and final diagnosis is determined by the expert. The obtained results show that the type-2 fuzzy expert system can diagnose leukemia with the average accuracy about 97%.
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 Leukemia & Lymphoma Society of Canada Website. http://www.llscanada.org/leukemia.
 Cancer Treatment Centers of America Website. https://www.cancercenter.com/leukemia/types.
 Frank Puppe, Systematic Introduction to Expert Systems, Knowledge Representations and Problem-Solving Methods, Springer-Verlag, 1993.
 J Liebowitz, Introduction to expert systems. Mitchell Publishing, 1988.
 George J.Klir, Bo Yuan, Fuzzy Sets and Fuzzy Logic: Theory and Application. Prentice Hall P T R, 1995.
 Sadatasl, Ali Akbar, Mohammad Hossein Fazel Zarandi, and Abolfazl Sadeghi. "A combined facility location and network design model with multi-type of capacitated links and backup facility and non-deterministic demand by fuzzy logic." Fuzzy Information Processing Society (NAFIPS), 2016 Annual Conference of the North American. IEEE, 2016.
 Alireza Sadeghian, Jerry M. Mendel, Hooman Tahayori (Editors), Advances in Type-2 Fuzzy Sets and Systems: Theory and Applications, Springer, 2013.
 Nilesh N Karnik, Jerry M Mendel, and Qilian Liang. Type-2 fuzzy logic systems. Fuzzy Systems, IEEE Transactions on, 7(6):643–658, 1999.
 Ali. Adeli, Mehdi. Neshat,” A Fuzzy Expert System for Heart Disease Diagnosis”, Proceedings of the International Multiconference of Engneers and Computer Scientists 2010 Vol I, IMECS 2010, march 17-19, 2010, Hong Kong.
 Kumar, Sanjeev, and Gursimranjeet Kaur. "Detection of heart diseases using fuzzy logic." Int. J. Eng. Trends Technol.(IJETT) 4.6 (2013).
 Allahverdi, Novruz, Serhat Torun, and Ismail Saritas. "Design of a fuzzy expert system for determination of coronary heart disease risk." Proceedings of the 2007 international conference on Computer systems and technologies. ACM, 2007.
 Kumar, Dr AV Senthil. "Diagnosis of heart disease using Advanced Fuzzy resolution Mechanism." International Journal of Science and Applied Information Technology (IJSAIT) 2.2 (2013): 22-30.
 Oad, Kantesh Kumar, Xu DeZhi, and Pinial Khan Butt. "A fuzzy rule based approach to predict risk level of heart disease." Global Journal of Computer Science and Technology 14.3-C (2014): 17.
 Lavanya, K., MA Saleem Durai, and N. Ch Sriman Narayana Iyengar. "Fuzzy rule based inference system for detection and diagnosis of lung cancer." International Journal of Latest Trends in Computing 2.1 (2011): 165-171.
 Malathi, A., and A. K. Santra. "Diagnosis of Lung cancer disease using neuro fuzzy logic." CARE Journal of applied research (2013).
 Farahani, Farzad Vasheghani, MH Fazel Zarandi, and Abbas Ahmadi. "Fuzzy rule based expert system for diagnosis of lung cancer." Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC), 2015 Annual Conference of the North American. IEEE, 2015.
 Saritas, Ismail, Novruz Allahverdi, and Ibrahim Unal Sert. "A fuzzy expert system design for diagnosis of prostate cancer." a a 1 (2003): 50.
 Balanică, Victor, et al. "Evaluation of breast cancer risk by using fuzzy logic." University Politehnica of Bucharest Scientific Bulletin, Series C 73.1 (2011): 53-64.
 Latha, K. C., et al. "Visualization of risk in breast cancer using fuzzy logic in matlab environment." International Journal of Computational Intelligence Techniques 4.1 (2013): 114.
 Patel, Ashish, et al. "Decision support system for the diagnosis of asthma severity using fuzzy logic." Proceedings of the International Multiconference of Engineers and Computer Scientists. Vol. 1. 2012.
 Anand, S. Krishna, R. Kalpana, and S. Vijayalakshmi. "Design and implementation of a fuzzy expert system for detecting and estimating the level of asthma and chronic obstructive pulmonary disease." World Applied Sciences Journal 23.2 (2013): 213-223.
 Zarandi, MH Fazel, et al. "A fuzzy rule-based expert system for diagnosing asthma." Scientia Iranica. Transaction E, Industrial Engineering 17.2 (2010): 129.
 Mayilvaganan M and K. Rajeswari, Human Blood Pressure Classification Analysis using Fuzzy Logic Control System in Datamining, by, International Journal of Emerging Trends & Technology in Computer Science , 3(1), 2014, 305-306.
 Chandra, Vishal, and Pinki Singh. "Fuzzy Based High Blood pressure Diagnosis." International Journal of Advanced Research in Computer Science & Technology 2.2 (2014): 1.
 Kaur, Rupinder, and Amrit Kaur. "Hypertension diagnosis using fuzzy expert system." International Journal of Engineering Research and Applications (IJERA) National Conference on Advances in Engineering and Technology, AET-29th March. 2014.
 Djam, X. Y., et al. "A fuzzy expert system for the management of Malaria." (2011).
 Onuwa, OJEME BLESSING. "Fuzzy expert system for malaria diagnosis." Oriental J. of Computer Science and Technology 7 (2014): 273-284.
 Sharma, Priyanka, et al. "Decision Support System for Malaria and Dengue Disease Diagnosis (DSSMD)." International Journal of Information and Computation Technology, 3 (7) (2013): 633-640.
 Kadhim, Mohammed Abbas, M. Afshar Alam, and Harleen Kaur. "Design and implementation of fuzzy expert system for back pain diagnosis." International Journal of Innovative Technology & Creative Engineering 1.9 (2011): 16-22.
 Zarei, Hassan, Ali Vahidian Kamyad, and Ali Akbar Heydari. "Fuzzy modeling and control of HIV infection." Computational and mathematical methods in medicine 2012 (2012).
 Imianvan, A. A., U. F. Anosike, and J. C. Obi. "An Expert System for the Intelligent Diagnosis of HIV/AIDs Using Fuzzy Cluster Means Algorithm." Global Journal of Computer Science and Technology (2011).
 Khanale, P. B., and R. P. Ambilwade. "A fuzzy inference system for diagnosis of hypothyroidism." Journal of Artificial Intelligence 4.1 (2011): 45-54.
 Biyouki, S. Amrollahi, I. B. Turksen, and MH Fazel Zarandi. "Fuzzy rule-based expert system for diagnosis of thyroid disease." Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2015 IEEE Conference on. IEEE, 2015.
 Hasan, Mir Anamul, and Ahsan Raja Chowdhury. "Human disease diagnosis using a fuzzy expert system." arXiv preprint arXiv:1006.4544 (2010).
 Govinda Rao, S., M. Eswara Rao, and D. Siva Prasad. "Fever Diagnosis Rule-Based Expert Systems." International Journal of Engineering Research & Technology (IJERT) 2.8 (2013).
 Hole, Komal R., and Vijay S. Gulhane. "Expert System for Diagnosis of Memory Low Diseases." International Journal 2.1 (2014).
 Singh, Sachidanand, et al. "Diagnosis of arthritis through fuzzy inference system." Journal of Medical systems 36.3 (2012): 1459-1468.
 Baig, Mirza Mansoor, Hamid Gholamhosseini, and Michael J. Harrison. "Fuzzy logic based smart anaesthesia monitoring system in the operation theatre." WSEAS Transactions on Circuits and Systems 11.1 (2012): 21-32.
 Chandra, Vishal. "Fuzzy expert system for migraine analysis and diagnosis." International Journal of Science and Research 3.6 (2014): 956-959.
 Ghahazi, M. Arabzadeh, et al. "Fuzzy rule based expert system for diagnosis of multiple sclerosis." Norbert Wiener in the 21st Century (21CW), 2014 IEEE Conference on. IEEE, 2014.
 Maftouni, Maede, et al. "Type-2 fuzzy rule-based expert system for Ankylosing spondylitis diagnosis." Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC), 2015 Annual Conference of the North American. IEEE, 2015.
 M.H. Fazel Zarandi, M. Zarinbal, M. Izadi, Systematic image processing for diagnosing brain tumors: A Type-II fuzzy expert system approach, 2009.
 Talukdar, Nur Alom, Daizy Deb, and Sudipta Roy. "Automated blood cancer detection using image processing based on fuzzy system." IJARSC, ISSN-2277-128X 4.8 (2014).
 H. Islami Nosrat Abadi, S. Mohsen Taheri, A Fuzzy Expert System for Diagnosis of Blood Cancer, in the proceeding of 6th International Conference of ICT Management.
 Obi, J. C., and A. A. Imianvan. "Interactive neuro-fuzzy expert system for diagnosis of leukemia." global journal of computer science and technology (2011).
 Noorizadeh, Maliheh, and Rahil Hosseini. "Acute Leukaemia Diagnosis using A Fuzzy Expert System." Proc. of the 14th Fuzzy Systems Association Conference, Tabriz, Iran. 2014.
 Harun, Nor Hazlyna, et al. "Unsupervised segmentation technique for acute leukemia cells using clustering algorithms." World Academy of Science, Engineering and Technology International Journal of Computer, Control, Quantum and Information Engineering 9 (2015): 253-59.
 Romel Bhattacharjee, White Blood Cells’ Segmentation for the Detection of Acute Lymphoblastic Leukemia, Journal of Basic and Applied Engineering Research, ISSN: 2350-0255; Volume 2, Number 2; January-March, 2015, pp. 99-103
 Priya, D. Kavi, et al. "Detection of leukemia in blood microscopic images using fuzzy logic." Int. J. Engg. Res. & Sci. & Tech 240 (2015).
 Purushotham, Swarnalatha, and B. K. Tripathy. "A Comparative Analysis of Depth Computation of Leukaemia Images using a Refined Bit Plane and Uncertainty Based Clustering Techniques." Cybernetics and Information Technologies 15.1 (2015): 126-146.
 Amin, Morteza Moradi, et al. "Recognition of acute lymphoblastic leukemia cells in microscopic images using k-means clustering and support vector machine classifier." Journal of medical signals and sensors 5.1 (2015): 49.
 Latifi, Farzaneh, et al. “Designing a Fuzzy Expert System and Nervous System for Computerized Diagnosis of Acute Lymphocytic Leukemia in Children Comparing Their Function.” Third International Conference on Applied Research in Computer Engineering and Information Technology, 2015.
 Sadat Asl, Ali Akbar, and Mohammad Hossein Fazel Zarandi. "A Type-2 Fuzzy Expert System for Diagnosis of Leukemia." North American Fuzzy Information Processing Society Annual Conference. Springer, Cham, 2017.
 Leukemia & Lymphoma Society Website. http://www.lls.org/managing-your-cancer/lab-and-imaging-tests/understanding-blood-counts
 Zarandi, MH Fazel, M. R. Faraji, and M. Karbasian. "An exponential cluster validity index for fuzzy clustering with crisp and fuzzy data." Scientia Iranica. Transaction E, Industrial Engineering 17.2 (2010): 95.
 Liang, Qilian, and Jerry M. Mendel. "Interval type-2 fuzzy logic systems: theory and design." IEEE Transactions on Fuzzy systems 8.5 (2000): 535-550.