Advances in Networks
Volume 7, Issue 2, December 2019, Pages: 45-50
Received: Oct. 20, 2019;
Accepted: Nov. 21, 2019;
Published: Nov. 27, 2019
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Labiga Laban Thomas, Department of Computer Science, Faculty of Physical Science, Modibbo Adama University of Technology, Yola, Nigeria
Ibrahim Goni, Department of Computer Science, Faculty of Science, Adamawa State University, Mubi, Nigeria
Gideon Daniel Emeje, Department of Computer Science, Faculty of Physical Science, Modibbo Adama University of Technology, Yola, Nigeria
Fuzzy logic lies in the ability to process nonlinear relationships. Because of the clinical complexity and pathologic heterogeneity of various diseases, correct identification of patients with active disease likely depends on the presence of a single defining feature. Hence, it is not surprising that standard linear statistical methodologies are relatively inadequate for medical diagnosis. In the medical field, dealing with diagnosis error and several levels of uncertainties and imprecision in the diagnoses of diseases had been a great challenge. To solve such problems, artificial intelligence gives a solution through expert system. Fuzzy Logic handles uncertainties, imprecisions and obscurity in decision making. Fuzzy logic is been preferred by Researchers because of its flexible structure and use of intuitive methods instead of specific algorithm. It deals with the degree of membership as it refers to the extent to which an event occurred or can occur. Fuzzy set uses the continuum of logical values between 0 and 1. Different Fuzzy models were reviewed. These systems diagnose many diseases such as: Malaria born infectious disease, Heart related diseases or cardiovascular diseases (like Atherosclerosis), cancer, Asthma, Lungs cancer, Cold and Flu, Hepatitis, Osteomyelitis and Meningitis. In the near future, medical service delivery will be more accessible and more efficient due to availability of Medical Diagnostic Systems.
Labiga Laban Thomas,
Gideon Daniel Emeje,
Fuzzy Models Applied to Medical Diagnosis: A Systematic Review, Advances in Networks.
Vol. 7, No. 2,
2019, pp. 45-50.
Oye, N. D and Thomas L. L. (2019), Fuzzy Model for Diagnosis of Bacterial Meningitis. International Journal of Computer Applications Technology and Research, Volume 8–Issue 02, 33-51, 2019, ISSN: -2319–8656.
Wan H., Wan I. and Fadzilah S., (2017), Artificial intelligence in medicalapplication: An exploration. P. 1 http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=3689031BE25EB5E5D92B30A2CEDF318?doi=10.1.1.102.4631&rep=rep1&type=pdf, retrieved 23/10/2017.
Djam, X. Y., Wajiga, G. M., Kimbi Y. H. and Blamah, N. V., (2011). A Fuzzy Expert System for the Management of Malaria. International Journal of Pure and Applied Sciences and Technology. ISSN 2229 – 6107, 5 (2) (2011), pp. 84-108.
Ahmed A. E. S., Sherif E. B. and Ahmed A. B. A., (2011). A Fuzzy Decision Support System for Management of Breast Cancer, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 2, No. 3, March 2011.
Awotunde J. B, Matiluko O. E and Fatai O. W. (2014). Medical Diagnosis System Using Fuzzy Logic. African Journal of Computing & ICT Reference Format: Afr J. of Comp & ICTs. Vol 7, No. 2. Pp 99-106.
Blej M. and Azizi M. (2016), Comparison of Mamdani-Type and Sugeno-Type Fuzzy Inference Systems for Fuzzy Real Time Scheduling. International Journal of Applied Engineering Research ISSN 0973-4562 Vol. 11, Number 22, pp. 11071-11075.
Josephine M. S. and Jeyabalaraja V. (2012), Expert System and Knowledge Management for Software Developer in Software Companies. InternationalJournal of Information and Communication Technology Research. Volume 2 No. 3, March 2012, ISSN: 2223-4985. In Chefi Ketata, Maria C. Rockwell and Denis Riordan, “Development of Expert Systems for Stream Sampling in Mineral Processing Plants”, Artificial Intelligence in Engineering, 14: 2, 2000.
Yilmaz M. (2015). Evaluation of Total Antioxidant Capacity (TAS) by Using Fuzzy Logic. British Journal of Mathematics & Computer Science 8 (6): 433-446, 2015, Article no. BJMCS. 20 15176 ISSN: 2231-0851.
Banerjee S, Aishwaryaprajna, Debjani C, Amita G, Ranjan G, Badal C. S, Jyotirmoy C. (2016). Application of fuzzy consensus for oral pre-cancer and cancer susceptibility assessment. Egyptian Informatics Journal (2016), pp. 251–263.
Mankar N. B. and Nagdeve U. T. (2014). Analysis of Ulcer Wound Using Fuzzy Logic. International journal of engineering sciences & research Technology, Mankar, 3 (9): September, 2014. ISSN: 2277-96557: 16.
Mamta B. (2016), Study of Need and Framework of Expert Systems for Medical Diagnosis. IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661, p-ISSN: 2278-8727 PP 45-48.
Zadeh L. A. (1975). The Concept of a Linguistic Variable and its Application to ApproximateReasoning-I. Information sciences. American Elsevier Publishing Company, Inc., pp. 199-248.
Omar A. M. A., Aous Y. A, Balasem S. S. (2015), Comparison between the Effects of Different Types ofMembership Functions on Fuzzy Logic Controller Performance. Communication & Computer Engineering Department, Cihan University Erbil, Iraq. International Journal of Emerging Engineering Research and Technology Volume 3, Issue 3, March2015, PP 76-83.
Singh H, Madan M. G, Thomas M, Zeng-Guang H, Kum K. G, Ashu, Solo M. G. and Zadeh L. A. (2013). Real-Life Applications of Fuzzy Logic. Hindawi Publishing Corporation, Advances in FuzzySystems, Volume 2013, Article ID 581879, 3pages, http://dx.doi.org/10.1155/2013/581879.
Teodorescu H. N. (2018), Perspectives in Fuzzy Logic and Fuzzy Systems. Romanian Journal of Information Science and Technology, Vol. 20, Number 4, pp. 324–327.
Santosh K. P, Dipti P. S. and Indrajit M. (2010). An Expert System for Diagnosis of Human Diseases. 2010 International Journal of Computer Applications (0975– 8887) Volume 1– No. 13.
Krishna A. S, Kalpana R and Vijayalakshmi S. (2013), 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).
Priynka S, Singh DBV, Manoj K. B and Nidhi M (2013), Decision Support System for Malaria and Dengue Disease Diagnosis. International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 3, Number 7 (2013), pp. 633-640 © International Research Publications House.
Boruah I. and Kakoty S. (2019), Analytical Study of Data Mining Applications in Malaria Prediction and Diagnosis. International Journal of Computer Science and Mobile Computing. Vol. 8 Issue. 3, March-2019, pg. 275-284.
AL-Rahman M. E., Adu I. K. and Yang C. (2019), A Mathematical Model of Malaria Transmission in Democratic Republic of the Congo, Journal of Mathematical and Statistical Analysis. Vol 2: 1, 2019, pp. 1-14.
Kelley D. (2014). Heart Disease: Causes, Prevention, and Current Research. Johnson County Community College. JCCC Honors Journal Volume 5 Issue 2 Spring 2014 Article 1.
Lafta H. A. and Oleiwi W. K. (2017), A Fuzzy Petri Nets System for Heart Disease Diagnosis. Journal of Babylon University/Pure and Applied Sciences /No. (2)/Vol. (25): 2017. Retrieved on 01/11/2017.
Senthil Kumar A. V., (2013), Diagnosis of heart disease using Advanced Fuzzy resolution Mechanism International Journal of Science and Applied Information Technology (IJSAIT), Vol. 2, No. 2, Pages: 22-30.
Smita S. S., Sushil S. and Ali M. S. (2013), Generic Medical Fuzzy Expert System for Diagnosis of Cardiac Diseases. International Journal of Computer Applications (0975–8887), Volume 66–No. 13, March 2013, Pp. 35-44.
Mayilvaganan M and K. Rajeswari. Human Blood Pressure Classification Analysis Using Fuzzy Logic Control System in Datamining. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS). Volume 3, Issue 1, January–February 2014, ISSN 2278-6856. 2014, Pp: 305-306.
Ali A. and Mehdi N. (2010), a Fuzzy Expert System for Heart Disease Diagnosis. Proceedings of the International MultiConference of Engineers and Computer Scientist. 2010 Vol. I, IMEC S2010, March 17-19, 2010, Hong Kong. ISBN: 978-988-17012-8-2. ISSN: 20178 -0958 (print); ISSN: 2078-0988 (online).
Hassan, N., Sayed, O. R., Khalil, A. M., & Ghany, M. A. (2017). Fuzzy Soft Expert System in Prediction of Coronary Artery Disease. International Journal of Fuzzy Systems, 19 (5), 1546-1559. DOI: 10.1007/s40815-016-0255-0.
Peláez-Aguilera M. D., Espinilla M., Olmo M. F. and Medina J. (2019), Fuzzy Linguistic Protoforms to Summarize Heart Rate Streams of Patients with Ischemic Heart Disease. Hindawi Complexity, Vol. 2019, pp. 1-11. https://doi.org/10.1155/2019/2694126.
Erin B. and Abiyev R. H. (2019), International Conference on Machine Learning and Soft Computing. Da Lat, VietNam, Association for Computing Machinery. Pp. 239-243 https://doi.org/10.1145/3310986.3311028.
Ahmad G., Khan M. A., Abbas S., Athar A., Khan B. S., and Aslam M. S. (2019), Automated Diagnosis of Hepatitis B Using Multilayer Mamdani Fuzzy Inference System. Journal of Healthcare Engineering, Volume 2019, Article ID 6361318, Pp. 1-11. https://doi.org/10. 1155/2019/6361318.
Vijay K. M, Ravinder M., Ryan W, and Elpiniki I P.(2012), Supporting meningitis diagnosis amongst infants and children through the use of fuzzy cognitive mapping. Mago et al. BMC Medical Informatics and Decision Making 2012, 12: 98.
Mostafa L, Esmat K, Hassan K, Mehrdad F and Mahdi E. (2015). A Fuzzy Expert System for Distinguishing between Bacterial and Aseptic Meningitis. Iranian Journal of Medical Physics. Vol. 12, No. 1. Winter 2015, Pp: 1-6.
Esshaghi A, Kamyad A. V, Heydari A. A. and Heydari A. (2019), Differential Diagnostic of Crimean Congo Hemorrhagic Fever, Influenza and Bacterial Meningitis by Classic and Fuzzy Mathematics. International Journal of Infection. Vol 6 (3), pp. 1-6.
Aqeel M. H, (2016), Lung Cancer diagnosis by using Fuzzy Logic. International Journal of Computer Science and Mobile Computing, Vol. 5. Issue. 3, March- 2016, pp. 32-41.
Alcantud J. C. R., Varela G., Santos-Buitrago B., Santos-Garcıa G and Jimenez M. F. (2019), Analysis of survival for lung cancer resections cases with fuzzy and soft set theory in surgical decision making. PLoS ONE 14 (6): e0218283. https://doi.org/ 10.1371/journal pone.0218283.
Malathi A. and Santra A. K. (2013). Diagnosis of Lung Cancer Disease using Neuro-Fuzzy Logic. CARE Journal of Applied Research (ISSN 2321-4090).
Lavanya K., Saleem D. M. A., Sriman N. I. N. Ch. (2011). Fuzzy Rule Based Inference System for Detection and Diagnosis of Lung Cancer. International Journal of Latest Trends in Computing (E-ISSN: 2045-5364) 166. Volume 2, Issue 1. Pp: 165-171.
Olatunde k. V. and Aderinto Y. O. (2017), Fuzzy Model for Osteomyelitis Severity Prediction. FUTA Journal of Research in Sciences, Vol. 13 (2) October, 2017: 337-342.
Ceyhun V., Bilgehan A. A., Turkan A. A. and Neslihan A. A. (2016), A case of osteomalacia initially followed as restless leg syndrome for 6 months. Biomedical Research, 2016. 27 (4): pp: 1284-1287.
Zirra P. B, Umar T. M and Wallace E. O. (2016), A Fuzzy Based System for Determining the Severity Level of Osteomyelitis. International Journal of Advanced Research in Computer Science and Software Engineering 6 (6), June- 2016, pp. 174-183.
Gumpy J M, Ibrahim G and Umar T. M (2017), Adaptive Neuro-fuzzy System for Determining the Severity Level of Osteomyelitis and Control. Archives of Applied Science Research, 2017, 9 (2): 9-15.
Emuoyibofarhe, O. J. and Taiwo, K. F. (2012), Fuzzy-Based System for Determining the Severity Level of Knee Osteoarthritis. International Journal of Intelligent Systems and Applications. 4 (9): 46-53.