Diseases diagnosis using fuzzy logic methods: A systematic and meta-analysis review

Computer Methods and Programs in Biomedicine

Volumn 161, Issue , 2018, Pages 145-172

  Ahmadi, Hossein ^a,b   Gholamzadeh, Marsa ^b   Shahmoradi, Leila ^b   Nilashi, Mehrbakhsh ^c,e,f   Rashvand, Pooria ^d  

^a IRAN UNIVERSITY OF MEDICAL SCIENCES   (Iran)

^b TEHRAN UNIVERSITY OF MEDICAL SCIENCES   (Iran)

^c UNIVERSITI TEKNOLOGI MALAYSIA   (Malaysia)

^d ISLAMIC AZAD UNIVERSITY   (Iran)

^e ISLAMIC AZAD UNIVERSITY   (Iran)

^f ISLAMIC AZAD UNIVERSITY   (Iran)

Author keywords

Disease diagnosis; Fuzzy logic; Fuzzy methods; PRISMA; Uncertainty

Indexed keywords

COMPUTER CIRCUITS; DECISION MAKING; DIAGNOSIS; MEDICAL PROBLEMS;

CLINICAL DECISION MAKING; DISEASE DIAGNOSIS; FUZZY METHODS; INCLUSION AND EXCLUSIONS; MEDICAL DISCIPLINES; PRISMA; SCIENTIFIC DATABASE; UNCERTAINTY;

FUZZY LOGIC;

CLINICAL FEATURE; DATA BASE; DECISION SUPPORT SYSTEM; DIAGNOSIS; DISEASES; FUZZY DECISION TREE; FUZZY LOGIC; FUZZY NEURAL NETWORK; FUZZY SYSTEM; HUMAN; MEDICAL LITERATURE; MEDICAL PRACTICE; META ANALYSIS; REVIEW; SYSTEMATIC REVIEW; ALGORITHM; CLINICAL DECISION SUPPORT SYSTEM; DIAGNOSTIC IMAGING; IMAGE PROCESSING; MACHINE LEARNING; PROCEDURES; SOFTWARE; THEORETICAL MODEL; UNCERTAINTY;

ALGORITHMS; DECISION SUPPORT SYSTEMS, CLINICAL; DIAGNOSTIC IMAGING; FUZZY LOGIC; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MACHINE LEARNING; MODELS, THEORETICAL; SOFTWARE; UNCERTAINTY;

EID: 85046134112     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2018.04.013     Document Type: Review

References (159)

1. Armstrong, N., Hilton, P., Doing diagnosis: whether and how clinicians use a diagnostic tool of uncertain clinical utility. Soc. Sci. Med. 120 (2014), 208–214.
2. Scully, J.L., What is a disease?. EMBO Rep. 5 (2004), 650–653.
3. Leaman, R., Islamaj Doğan, R., Lu, Z., DNorm: disease name normalization with pairwise learning to rank. Bioinformatics 29 (2013), 2909–2917.
4. Scheuermann, R.H., Ceusters, W., Smith, B., Toward an ontological treatment of disease and diagnosis. Summit Transl. Bioinform. 2009 (2009), 116–120.
5. Croft, P., Altman, D.G., Deeks, J.J., Dunn, K.M., Hay, A.D., Hemingway, H., LeResche, L., Peat, G., Perel, P., Petersen, S.E., Riley, R.D., Roberts, I., Sharpe, M., Stevens, R.J., Van Der Windt, D.A., Von Korff, M., Timmis, A., The science of clinical practice: disease diagnosis or patient prognosis? Evidence about “what is likely to happen” should shape clinical practice. BMC Med., 13, 2015, 20.
6. Committee on Diagnostic Error in Health Care, Board on Health Care Services, Institute of Medicine, T.N.A.o. Sciences, Improving Diagnosis in Health Care. 2015, The National Academies Press, Washington, DC.
7. Barro, S., Marin, R., Fuzzy Logic in Medicine, 2013.
8. Torres, A., Nieto, J.J., Fuzzy logic in medicine and bioinformatics. J. Biomed. Biotechnol., 2006, 2006, 91908.
9. Alam, R., Cheraghi-Sohi, S., Panagioti, M., Esmail, A., Campbell, S., Panagopoulou, E., Managing diagnostic uncertainty in primary care: a systematic critical review. BMC Family Pract., 18, 2017, 79.
10. Malmir, B., Amini, M., Chang, S.I., A medical decision support system for disease diagnosis under uncertainty. Expert Syst. Appl. 88 (2017), 95–108.
11. Nilashi, M., bin Ibrahim, O., Ahmadi, H., Shahmoradi, L., An analytical method for diseases prediction using machine learning techniques. Comput. Chem. Eng.106, 106 (2017), 212–223.
12. Nilashi, M., Ibrahim, O., Ahmadi, H., Shahmoradi, L., Farahmand, M., A hybrid intelligent system for the prediction of Parkinson's disease progression using machine learning techniques. Biocybern. Biomed. Eng. 38 (2018), 1–15.
13. Nilashi, M., Ahmadi, H., Shahmoradi, L., Mardani, A., Ibrahim, O., Yadegaridehkordi, E., Knowledge discovery and diseases prediction: a comparative study of machine learning techniques. J. Soft Comput. Decis. Support Syst. 4 (2017), 8–16.
14. Nilashi, M., Ibrahim, O., Dalvi, M., Ahmadi, H., Shahmoradi, L., Accuracy improvement for diabetes disease classification: a case on a public medical dataset. Fuzzy Inf. Eng. 9 (2017), 345–357.
15. Nilashi, M., Ahmadi, H., Shahmoradi, L., Salahshour, M., Ibrahim, O., A soft computing method for mesothelioma disease classification. J. Soft Comput. Decis. Support Syst. 4 (2017), 16–18.
16. Zadeh, L., Fuzzy sets. Inform. Control 8 (1965), 338–353.
17. Zadeh, L., Fuzzy logic—a personal perspective. Fuzzy Sets Syst. 281 (2015), 4–20.
18. Gürsel, G., Healthcare, uncertainty, and fuzzy logic. Digit. Med. 2 (2016), 101–112.
19. Seising, R., Views on fuzzy sets and systems from different perspectives. Philosophy and Logic, Criticisms and Application, 2009, Springer.
20. Seising, R., Tabacchi, M.E., Fuzziness and medicine: philosophical reflections and application systems in health care. A Companion Volume to Sadegh-Zadeh's Handbook of Analytical Philosophy of Medicine, 2013, Springer, Berlin Heidelberg.
21. Dragović, Ivana, Turajlić, Nina, Pilčević, Dejan, Petrović, Bratislav, Radojević, Dragan, A boolean consistent fuzzy inference system for diagnosing diseases and its application for determining peritonitis likelihood. Comput. Math. Methods Med., 2015, 2015, 11.
22. Godil, S.S., Shamim, M.S., Enam, S.A., Qidwai, U., Fuzzy logic: a “simple” solution for complexities in neurosciences?. Surg. Neurolo. Int., 2, 2011, 24.
23. Yilmaz, A., Ayan, K., Early diagnosis in cancer disease by using fuzzy logic and the effects of stress on cancer. Int. Rev. Comput. Softw. 6 (2011), 347–354.
24. Sadegh-Zadeh, K., Handbook of Analytic Philosophy of Medicine. 2015, Springer, Netherlands.
25. Didier, D., Henry, P., The legacy of 50 years of fuzzy sets: a discussion. Fuzzy Sets Syst. 281 (2015), 21–31.
26. Hunter, D.J., Gene-environment interactions in human diseases. Nat. Rev. Genet. 6 (2005), 287–298.
27. Romero-Córdoba, R., Olivas, J.A., Romero, F.P., Alonso-Gonzalez, F., Serrano-Guerrero, J., An application of fuzzy prototypes to the diagnosis and treatment of fuzzy diseases. Int. J. Intell. Syst. 32 (2017), 194–210.
28. Sadegh-Zadeh, K., Fuzzy health, illness, and disease. J. Med. Philos. 25 (2000), 605–638.
29. Limberg, J., Seising, R., Fuzzy health, illness, and disease Sadegh–Zadeh's framework and a program to identify diseases. Proceedings of IEEE International Fuzzy Systems Conference, FUZZ-IEEE 2007, 2007, IEEE, 1–6.
30. Whang, W., Medical decision-making. Gellman, M.D., Turner, J.R., (eds.) Encyclopedia of Behavioral Medicine, 2013, Springer, New York, NY, 1209–1210.
31. Hausmann, D., Zulian, C., Battegay, E., Zimmerli, L., Tracing the decision-making process of physicians with a Decision Process Matrix. BMC Med. Inform. Decis. Mak., 16, 2016, 133.
32. Monteiro, S.M., Norman, G., Diagnostic reasoning: where we've been, where we're going. Teach. Learn. Med., 25(Suppl 1), 2013 S26-32.
33. Norman, G., Barraclough, K., Dolovich, L., Price, D., Iterative diagnosis. BMJ 339 (2009), 747–752.
34. Thammasitboon, S., Cutrer, W.B., Diagnostic decision-making and strategies to improve diagnosis. Curr. probl. Pediatr. Adolesc. Health Care 43 (2013), 232–241.
35. Thammasitboon, S., Thammasitboon, S., Singhal, G., Diagnosing diagnostic error. Curr. probl. Pediatr. Adolesc. Health Care 43 (2013), 227–231.
36. Kumar, A.V.S., Fuzzy expert systems for disease diagnosis. IGI Glob., 2014.
37. Elisabeth, Rakus-Andersson, Fuzzy and Rough Techniques in Medical Diagnosis and Medication. 2007, Springer.
38. Al-Absi, H.R.H., Abdullah, A., Hassan, M.I., Soft computing in medical diagnostic applications: a short review. Proceedings of 2011 National Postgraduate Conference, 2011, 1–5.
39. Bhatia, A., Mago, V., Singh, R., Use of soft computing techniques in medical decision making: a survey. Proceedings of 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2014, 1131–1137.
40. Ezequiel, L.-R., Elizondo David, A., Martin, G., Jerez, J.M., Luque-Baena, R.M., Computational intelligence techniques in medicine. Comput. Math. Methods Med. 2015 (2015), 1–2.
41. Phuong, N.H., Kreinovich, V., Fuzzy logic and its applications in medicine. Int. J. Med. Inform. 62 (2001), 165–173.
42. Brust-Renck, P.G., Reyna, V.F., Wilhelms, E.A., Lazar, A.N., A Fuzzy-Trace theory of judgment and decision-making in health care: explanation, prediction, and application. Diefenbach, M.A., Miller-Halegoua, S., Bowen, D.J., (eds.) Handbook of Health Decision Science, 2016, Springer, New York, New York, NY, 71–86.
43. Roveri, M.I., Manoel, E.d.J., Onodera, A.N., Ortega, N.R.S., Tessutti, V.D., Vilela, E., Evêncio, N., Sacco, I.C.N., Assessing experience in the deliberate practice of running using a fuzzy decision-support system. PLoS ONE, 12, 2017, e0183389.
44. Alayón, S., Robertson, R., Warfield, S.K., Ruiz-Alzola, J., A Fuzzy system for helping medical diagnosis of malformations of cortical development. J. Biomed. Inform. 40 (2007), 221–235.
45. Sanchez, E., Inverses of fuzzy relations: application to possibility distributions and medical diagnosis. Proceedings of IEEE Conference on Decision and Control including the sixteenth Symposium on Adaptive Processes and A Special Symposium on Fuzzy Set Theory and Applications, 1977, 1977, IEEE, 1384–1389.
46. De, S.K., Biswas, R., Roy, A.R., An application of intuitionistic fuzzy sets in medical diagnosis. Fuzzy Sets Syst. 117 (2001), 209–213.
47. Cook, D.A., Medical decision making: what do we trust?. J. Gen. Intern. Med. 25 (2010), 282–283.
48. Zolhavarieh, S., Parry, D., Bai, Q., Issues associated with the use of semantic web technology in knowledge acquisition for clinical decision support systems: systematic review of the literature. JMIR Med. Inform., 5, 2017, e18.
49. Dixon, B.E., Kasting, M.L., Wilson, S., Kulkarni, A., Zimet, G.D., Downs, S.M., Health care providers’ perceptions of use and influence of clinical decision support reminders: qualitative study following a randomized trial to improve HPV vaccination rates. BMC Med. Inform. Decis. Mak., 17, 2017, 119.
50. John, R.I., Innocent, P.R., Modeling uncertainty in clinical diagnosis using fuzzy logic. IEEE Trans. Syst. Man Cybern. Part B, Cybern. 35 (2005), 1340–1350 a publication of the IEEE Systems, Man, and Cybernetics Society.
51. D'Acierno, A., Esposito, M., De Pietro, G., An extensible six-step methodology to automatically generate fuzzy DSSs for diagnostic applications. BMC Bioinform., 14(Suppl 1), 2013, S4.
52. Blej, Mohammed, Aziz, M., Comparison of Mamdani-Type and Sugeno-Type Fuzzy inference systems for fuzzy real time scheduling. Int. J. Appl. Eng. Res. 11 (2016), 11071–11075.
53. Singla, J., Comparative study of Mamdani-type and Sugeno-type fuzzy inference systems for diagnosis of diabetes. Proceedings of 2015 International Conference On Advances In Computer Engineering and Applications, 2015, 517–522.
54. William, Siler, Buckley, J., Fuzzy Expert Systems and Fuzzy Reasoning. 2005, Wiley.
55. Ganji, M.F., Abadeh, M.S., A fuzzy classification system based on Ant Colony Optimization for diabetes disease diagnosis. Expert Syst. Appl. 38 (2011), 14650–14659.
56. Shen, S., Sandham, W.A., Granat, M.H., Dempsey, M.F., Patterson, J., Fuzzy clustering based applications to medical image segmentation. Proceedings of the Twenty Fifth Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439), 741, 2003, 747–750.
57. Mahfouf, M., Abbod, M.F., Linken, D.A., A survey of fuzzy logic monitoring and control utilisation in medicine. Artif. Intell. Med. 21 (2001), 27–42.
58. Salman, A., Lina, Y., Simon, C., Computational intelligence method for early diagnosis dengue haemorrhagic fever using fuzzy on mobile device. Proceedings of the EPJ Web of Conferences, 68, 2014, 00003.
59. Pandey, B., Mishra, R.B., Knowledge and intelligent computing system in medicine. Comput. Biol. Med. 39 (2009), 215–230.
60. Georgopoulos, V.C., Stylios, C.D., Fuzzy cognitive map decision support system for successful triage to reduce unnecessary emergency room admissions for the elderly. Seising, R., Tabacchi, M.E., (eds.) Fuzziness and Medicine: Philosophical Reflections and Application Systems in Health Care: A Companion Volume to Sadegh-Zadeh's Handbook of Analytical Philosophy of Medicine, 2013, Springer, Berlin Heidelberg, Berlin, Heidelberg, 415–436.
61. Innocent, P.R., John, R.I., Garibaldi, J.M., Fuzzy methods for medical diagnosis. Appl. Artif. Intell. 19 (2004), 69–98.
62. Straszecka, E., et al. On advantages of a fuzzy approach to a diagnosis support. Filev, D., Jabłkowski, J., Kacprzyk, J., Krawczak, M., Popchev, I., Rutkowski, L., et al. (eds.) Proceedings of the Seventh IEEE International Conference on Intelligent Systems IS’2014, Cham, 2015, Springer International Publishing, 345–355 September 24–26, 2014, Warsaw, Poland, Volume 2: Tools, Architectures, Systems, Applications.
63. Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., Stewart, L.A., Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst. Rev., 4, 2015, 1.
64. Hutton, B., Catalá-López, F., Moher, D., The PRISMA statement extension for systematic reviews incorporating network meta-analysis: PRISMA-NMA. Med. Clín. 147 (2016), 262–266 (English Edition).
65. Liberati, A., Altman, D.G., Tetzlaff, J., Mulrow, C., Gøtzsche, P.C., Ioannidis, J.P.A., Clarke, M., Devereaux, P.J., Kleijnen, J., Moher, D., The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ, 339, 2009.
66. Gopalakrishnan, S., Ganeshkumar, P., Systematic reviews and meta-analysis: understanding the best evidence in primary healthcare. J. Family Med. Prim. Care 2 (2013), 9–14.
67. Ressing, M., Blettner, M., Klug, S.J., Systematic Literature Reviews and Meta-Analyses: Part 6 Of a Series on Evaluation of Scientific Publications, 106, 2009, Deutsches Ärzteblatt International, 456–463.
68. Leucht, S., Kissling, W., Davis, J.M., How to read and understand and use systematic reviews and meta-analyses. Acta Psych. Scand. 119 (2009), 443–450.
69. Hogan, W.R., Ceusters, W., Diagnosis, misdiagnosis, lucky guess, hearsay, and more: an ontological analysis. J. Biomed. Semant., 7, 2016, 54.
70. Masic, I., Milinovic, K., On-line biomedical databases–the best source for quick search of the scientific information in the biomedicine. Acta Inform. Med. 20 (2012), 72–84.
71. Holla, J.F.M., Sanchez-Ramirez, D.C., van der Leeden, M., Ket, J.C.F., Roorda, L.D., Lems, W.F., Steultjens, M.P.M., Dekker, J., The avoidance model in knee and hip osteoarthritis: a systematic review of the evidence. J. Behav. Med. 37 (2014), 1226–1241.
72. Adeli, A., Neshat, M., A fuzzy expert system for heart disease diagnosis. Proceedings of International Multi Conference of Engineers and Computer Scientists. Hong Kong: IMECS Conference Proceedings, 2010.
73. Kahraman, C., Fuzzy Applications in Industrial Engineering (Studies in Fuzziness and Soft Computing). 2006, Springer-Verlag, New York Inc.
74. Leondes, C.T., Fuzzy Logic and Expert Systems Applications. 1997, Academic Press 11th December.
75. Çaydaş, U., Hasçalık, A., Ekici, S., An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM. Expert Syst. Appl. 36 (2009), 6135–6139.
76. Dragovic, I., Turajlic, N., Pilcevic, D., Petrovic, B., Radojevic, D., A Boolean consistent fuzzy inference system for diagnosing diseases and its application for determining peritonitis likelihood. Comput. Math. Methods Med., 2015, 2015, 147947.
77. Gayathri, B.M., Sumathi, C.P., Mamdani fuzzy inference system for breast cancer risk detection. Proceedings of 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 2015, 1–6.
78. Jang, J.S.R., ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23 (1993), 665–685.
79. Reshmalakshmi, C., Sasikumar, M., Fuzzy inference system for osteoporosis detection. Proceedings of 2016 IEEE Global Humanitarian Technology Conference (GHTC), 2016, 675–681.
80. Mosleh, M., Fuzzy neural network for solving a system of fuzzy differential equations. Appl. Soft Comput. 13 (2013), 3597–3607.
81. Chen, C.-F., Applying the analytical hierarchy process (AHP) Approach to convention site selection. J. Travel Res. 45 (2006), 167–174.
82. Kunhimangalam, R., Ovallath, S., Joseph, P.K., A clinical decision support system with an integrated EMR for diagnosis of peripheral neuropathy. J. Med. Syst., 38, 2014, 38.
83. Uncu, U., Evaluation of pulmonary function tests by using fuzzy logic theory. J. Med. Syst. 34 (2010), 241–250.
84. Oluwagbemi, O., Oluwagbemi, F., Abimbola, O., Ebinformatics: Ebola fuzzy informatics systems on the diagnosis, prediction and recommendation of appropriate treatments for Ebola virus disease (EVD). Inform. Med. Unlocked 2 (2016), 12–37.
85. Ramiro, M.-P., Alberto Aguilar-Lasserre, A., Ureña-Bogarín Enrique, L, Vázquez-Rodríguez Carlos, F., Rubén, P.-G., Armín, T.-M., Development of a fuzzy expert system for the nephropathy control assessment in patients with type 2 diabetes mellitus. Expert Syst. Appl. 72 (2016), 335–343.
86. Putra, A.A., Munir, R., Implementation of fuzzy inference system in children skin disease diagnosis application. Proceedings of International Conference on Electrical Engineering and Informatics (ICEEI), 2015, 365–370.
87. Ahmed, S., Tanzir Kabir, M., Mahmood, N.T., Rahman, R.M., Diagnosis of kidney disease using fuzzy expert system. Proceedings of The eighth International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014), 2014, 1–8.
88. Allahverdi, N., Akcan, T., A Fuzzy expert system design for diagnosis of periodontal dental disease. Proceedings of Fifth International Conference on Application of Information and Communication Technologies (AICT), 2011, 1–5.
89. Bell, B.P., Damon, I.K., Jernigan, D.B., Kenyon, T.A., Nichol, S.T., O'Connor, J.P., Tappero, J.W., Overview, control strategies, and lessons learned in the CDC response to the 2014-2016 Ebola Epidemic. MMWR Suppl. 65 (2016), 4–11.
90. Mohammadpour, R.A., Abedi, S.M., Bagheri, S., Ghaemian, A., Fuzzy rule-based classification system for assessing coronary artery disease. Comput. Math. Methods Med., 2015, 2015, 8.
91. Roubos, J.A., Setnes, M., Abonyi, J., Learning fuzzy classification rules from labeled data. Inf. Sci. 150 (2003), 77–93.
92. Dubois, D., Prade, H., What are fuzzy rules and how to use them. Fuzzy Sets Syst. 84 (1996), 169–185.
93. Grosan, C., Abraham, A., Intelligent Systems: A Modern Approach. 2011, Springer-Verlag, Berlin Heidelberg.
94. Kim, M.W., Ryu, J.W., Optimized fuzzy classification using genetic algorithm. Proceedings of International Conference on Fuzzy Systems and Knowledge Discovery, 2005, Springer, 392–401.
95. Perfilieva, I., Analytical theory of fuzzy IF-THEN rules with compositional rule of inference. Wang, P.P., Ruan, D., Kerre, E.E., (eds.) Fuzzy Logic: A Spectrum of Theoretical & Practical Issues, 2007, Springer, Berlin Heidelberg, 174–191 Berlin, Heidelberg.
96. Ruan, D., Kerre, E.E., Fuzzy If-Then Rules in Computational Intelligence: Theory and Applications. 2012, Springer, US.
97. Arslan, E., Yildiz, S., Albayrak, Y., Koklukaya, E., Rule based fuzzy logic approach for classification of fibromyalgia syndrome. Australas. Phys. Eng. Sci. Med. 39 (2016), 501–515.
98. Leite, C.R., Sizilio, G.R., Neto, A.D., Valentim, R.A., Guerreiro, A.M., A fuzzy model for processing and monitoring vital signs in ICU patients. Biomed. Eng. Online, 10, 2011, 68.
99. Reddy, G.T., Khare, N., An efficient system for heart disease prediction using hybrid OFBAT with rule-based fuzzy logic model. J. Circuits Syst. Comput., 26(4), 2017, 1750061.
100. Nilashi, M., Ibrahim, O., Ahmadi, H., Shahmoradi, L., A knowledge-based system for breast cancer classification using fuzzy logic method. Telemat. Inform. 34 (2017), 133–144.
101. Akbarzadeh-T, M., Moshtagh-Khorasani, M., A hierarchical fuzzy rule-based approach to aphasia diagnosis. J. Biomed. Inform. 40 (2007), 465–475.
102. Biyouki, S.A., Turksen, I.B., Zarandi, M.H.F., Fuzzy rule-based expert system for diagnosis of thyroid disease. Proceedings of IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2015, 1–7.
103. Hung, K.-C., Tuan, H.-W., Medical diagnosis based on intuitionistic fuzzy sets revisited. J. Interdiscip. Math. 16 (2013), 385–395.
104. Castillo, O., Melin, P., Ross, O.M., Theoretical Advances and Applications of Fuzzy Logic and Soft Computing. 2007, Springer.
105. Wagholikar, K., Deshpande, A., Fuzzy Naïve Bayesian Approach for Medical Decision Support, Medical Applications of Artificial Intelligence. 2013, CRC Press, 267–278.
106. Khalaf, M.M., Medical diagnosis via interval valued intuitionistic fuzzy sets. Ann. Fuzzy Math. Inform. 6 (2013), 245–249.
107. Sacco, I.C., Hamamoto, A.N., Tonicelli, L.M., Watari, R., Ortega, N.R., Sartor, C.D., Abnormalities of plantar pressure distribution in early, intermediate, and late stages of diabetic neuropathy. Gait Posture 40 (2014), 570–574.
108. De Moraes Lopes, M.H., Siqueira Ortega, N.R., Massad, E., De Fatima Marin, H., Model for differential nursing diagnosis of alterations in urinary elimination based on fuzzy logic. Comput. Inform. Nurs.: CIN 27 (2009), 324–329.
109. Kannathal, N., Acharya, U.R., Ng, E.Y., Krishnan, S.M., Min, L.C., Laxminarayan, S., Cardiac health diagnosis using data fusion of cardiovascular and haemodynamic signals,. Comput. Methods Programs Biomed. 82 (2006), 87–96.
110. Duarte, P.S., Mastrocolla, L.E., Farsky, P.S., Sampaio, C.R., Tonelli, P.A., Barros, L.C., Ortega, N.R., Pereira, J.C., Selection of patients for myocardial perfusion scintigraphy based on fuzzy sets theory applied to clinical-epidemiological data and treadmill test results. Braz. J. Med. Biol. Res.=Rev. Brasileira Pesqui. Med. Biol. 39 (2006), 9–18.
111. Tsai, D.-Y., Kojima, K., Measurements of texture features of medical images and its application to computer-aided diagnosis in cardiomyopathy. Measurement 37 (2005), 284–292.
112. Faisal, T., Taib, M.N., Ibrahim, F., Adaptive neuro-fuzzy inference system for diagnosis risk in dengue patients. Expert Syst. Appl. 39 (2012), 4483–4495.
113. Najafi, M.R., Moradkhani, H., Wherry, S., Optimal selection of GCM predictors combined with a new procedure for statistical downscaling of precipitation. Proceedings of World Environmental and Water Resources Congress 2010: Challenges of Change, 2010, 4598–4607.
114. Fazeli, S., Naghibolhosseini, M., Bahrami, F., An adaptive Neuro-Fuzzy inference system for diagnosis of Aphasia. Proceedings of Second International Conference on Bioinformatics and Biomedical Engineering, 2008, 535–538.
115. Nazmy, T.M., El-Messiry, H., Al-Bokhity, B., Adaptive Neuro-Fuzzy Inference System for classification of ECG signals. Proceedings of the Seveth International Conference on Informatics and Systems (INFOS), 2010, 1–6.
116. Yilmaz, A., Ari, S., Kocabicak, U., Risk analysis of lung cancer and effects of stress level on cancer risk through neuro-fuzzy model. Comput. Methods Programs Biomed. 137 (2016), 35–46.
117. Mansoor Baig, M., Gholamhosseini, H., Harrison, M.J., Fuzzy logic based anaesthesia monitoring systems for the detection of absolute hypovolaemia. Comput. Biol. Med. 43 (2013), 683–692.
118. Ubeyli, E.D., Guler, I., Automatic detection of erthemato-squamous diseases using adaptive neuro- fuzzy inference systems. Comput. Biol. Med. 35 (2005), 421–433.
119. Derya Ubeyli, E., Guler, I., Adaptive neuro-fuzzy inference systems for analysis of internal carotid arterial Doppler signals. Comput. Biol. Med. 35 (2005), 687–702.
120. Shariati, S., Haghighi, M.M., Comparison of ANFIS neural network with several other ANNs and Support Vector Machine for diagnosing hepatitis and thyroid diseases. Proceedings of International Conference on Computer Information Systems and Industrial Management Applications (CISIM), 2010, 596–599.
121. Wang, C.-Y., Tsai, J.-T., Fang, C.-H., Lee, T.-F., Chou, J.-H., Predicting survival of individual patients with esophageal cancer by adaptive neuro-fuzzy inference system approach. Appl. Soft Comput. 35 (2015), 583–590.
122. Nilashi, M., Ibrahim, O., Ahani, A., Accuracy improvement for predicting Parkinson's disease progression. Sci. Rep., 6, 2016, 34181.
123. Li, L., Liqing, H., Hongru, L., Feng, Z., Chongxun, Z., Pokhrel, S., Jie, Z., The use of fuzzy backpropagation neural networks for the early diagnosis of hypoxic ischemic encephalopathy in newborns. J. Biomed. Biotechnol. 2011 (2011), 349–490.
124. Beganovic, E., Avdagic, K., Modeling of voting expert system prototype in classification of acid-base disorders. Proceedings of XXV International Conference on Information, Communication and Automation Technologies (ICAT), 2015, 1–8.
125. Yuan, Y., Shaw, M.J., Induction of fuzzy decision trees. Fuzzy Sets Syst. 69 (1995), 125–139.
126. Exarchos, T.P., Tsipouras, M.G., Exarchos, C.P., Papaloukas, C., Fotiadis, D.I., Michalis, L.K., A methodology for the automated creation of fuzzy expert systems for ischaemic and arrhythmic beat classification based on a set of rules obtained by a decision tree. Artif. Intell. Med. 40 (2007), 187–200.
127. Marsala, C., A fuzzy decision tree based approach to characterize medical data. Proceedings of IEEE International Conference on Fuzzy Systems, 2009, 1332–1337.
128. Fan, C.-Y., Chang, P.-C., Lin, J.-J., Hsieh, J.C., A hybrid model combining case-based reasoning and fuzzy decision tree for medical data classification. Appl. Soft Comput. 11 (2011), 632–644.
129. Kadi, I., Idri, A., Cardiovascular Dysautonomias diagnosis using crisp and fuzzy decision tree: a comparative study. Stud. Health Technol. Inform. 223 (2016), 1–8.
130. Levashenko, V., Zaitseva, E., Fuzzy Decision Trees in medical decision Making Support System. Proceedings of Federated Conference on Computer Science and Information Systems (FedCSIS), 2012, 213–219.
131. Kamadi, V.S.R.P.V., Allam, A.R., Thummala, S.M., V.N.R. P, A computational intelligence technique for the effective diagnosis of diabetic patients using principal component analysis (PCA) and modified fuzzy SLIQ decision tree approach. Appl. Soft Comput. 49 (2016), 137–145.
132. Romero-Córdoba, R., Olivas, J.Á., Romero, F.P., Alonso-Gómez, F., et al. Clinical decision support system for the diagnosis and treatment of fuzzy diseases. Puerta, J.M., Gámez, J.A., Dorronsoro, B., Barrenechea, E., Troncoso, A., Baruque, B., et al. (eds.) Proceedings of sixteenth Conference of the Spanish Association for Artificial Intelligence, Advances in Artificial Intelligence: CAEPIA 2015, Cham, 2015, Springer International Publishing, 128–138 Albacete, Spain, November 9–12, 2015.
133. Paul, A.K., Shill, P.C., Rabin, M.R.I., Kundu, A., Akhand, M.A.H., Fuzzy membership function generation using DMS-PSO for the diagnosis of heart disease. Proceedings of eighteenth International Conference on Computer and Information Technology (ICCIT), 2015, 456–461.
134. Efosa, I.C., Akwukwuma, V., Knowledge-based fuzzy inference system for sepsis diagnosis. Int. J. Comput. Sci. Inf. Technol. (IJCSITY) 1 (2013), 1–7.
135. Binaghi, E., A fuzzy logic inference model for a rule-based system in medical diagnosis. Expert Syst. 7 (1990), 134–141.
136. Schmitt, M., Teodorescu, H.N., Jain, A., Jain, S., Computational Intelligence Processing in Medical Diagnosis. 2013, Physica, Verlag HD.
137. Association, I.R.M., Fuzzy systems: concepts, methodologies, tools, and applications: concepts, methodologies, tools, and applications. Proceedings of IGI Global, 2017.
138. Iliadis, L.S., Papadopoulos, H., Jayne, C., Proceedings of Fourteenth International Conference on Engineering Applications of Neural Networks: EANN 2013, Halkidiki, GreeceBerlin Heidelberg, 2013, Springer September 2013, Proceedings.
139. Sari, W.E, Wahyunggoro, O., Fauziati, S., A comparative study on fuzzy Mamdani–Sugeno–Tsukamoto for the childhood tuberculosis diagnosis. AIP Conference Proceedings, AIP Publishing, 1741, 2016, 070003.
140. Sardesai, A., Kharat, V., Sambarey, P., Deshpande, A., Fuzzy logic-based formalisms for gynecology disease diagnosis. J. Intell. Syst. 25 (2016), 283–295.
141. Krashenyi, I., Popov, A., Ramirez, J., Gorriz, J.M., Application of fuzzy logic for Alzheimer's disease diagnosis. Proceedings of Signal Processing Symposium (SPSympo), Poland, 2015.
142. Saikia, D., Dutta, J.C., Early diagnosis of dengue disease using fuzzy inference system. Proceedings of International Conference on Microelectronics, Computing and Communications (MicroCom), 2016, 1–6.
143. Byczkowska-LipiŃSka, L., Fuzzy classification of medical data derived from diagnostic devices. Przegl Ąd Elektrotechniczny 1 (2016), 87–90.
144. Mittal, M., Shah, N.H., Optimal inventory control and management techniques. Proceedings of IGI Global, 2016.
145. Krishnaiah, V., Srinivas, M., Narsimha, G., Chandra, N.S., Diagnosis of heart disease patients using fuzzy classification technique. Proceedings of International Conference on Computing and Communication Technologies, 2014, 1–7.
146. Elshazly, H.I., Waly, M., Elkorany, A.M., Hassanien, A.E., Chronic eye disease diagnosis using ensemble-based classifier. Proceedings of International Conference on Engineering and Technology (ICET), 2014, 1–6.
147. Cheruku, R., Edla, D.R., Kuppili, V., Dharavath, R., RST-BatMiner: a fuzzy rule miner integrating rough set feature selection and Bat optimization for detection of diabetes disease. Appl. Soft Comput., 2017, 10.1016/j.asoc.2017.06.032 In press.
148. Stylios, C.S., Georgopoulos, V.C., Fuzzy cognitive maps for medical diagnosis support A paradigm from obstetrics. Proceedings of Thirty Second Annual International Conference of the IEEE EMBSBuenos Aires, Argentina, 2010 September 4.
149. Pop, H.F., Pop, T.L., Sarbu, C., Assessment of heart disease using fuzzy classification techniques. Sci. World J. 1 (2001), 369–390.
150. Amirkhani, A., Papageorgiou, E.I., Mohseni, A., Mosavi, M.R., A review of fuzzy cognitive maps in medicine. Comput. Methods Prog. Biomed. 142 (2017), 129–145.
151. Shyi-Ming, C., Chung-Ming, H., Generating weighted fuzzy rules from relational database systems for estimating values using genetic algorithms. IEEE Trans. Fuzzy Syst. 11 (2003), 495–506.
152. Gámez, J.A., Puerta, J.M., Learning weighted linguistic fuzzy rules by using specifically-tailored hybrid estimation of distribution algorithms. Int. J. Approx. Reas. 50 (2009), 541–560.
153. Anooj, P.K., Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules. J. King Saud Univ. - Comput. Inf. Sci. 24 (2012), 27–40.
154. Kabadayi, Nihan, Cirpin, BirgulKucuk, Service Quality Analysis with Using a Fuzzy AHP Methodology: a case study in Veterinary Hospital in Turkey. Proceedings of the 2016 International Conference on Industrial Engineering and Operations ManagementUSA, 2016.
155. Uzoka, F.-M.E., Obot, O., Barker, K., Osuji, J., An experimental comparison of fuzzy logic and analytic hierarchy process for medical decision support systems. Comput. Methods Program. Biomed. 103 (2011), 10–27.
156. Guerra, T.M., Sala, A., Tanaka, K., Fuzzy control turns 50: 10 years later. Fuzzy Sets Syst. 281 (2015), 168–182.
157. Mahmoud, M.S., Basics of Fuzzy Control, Fuzzy Control, Estimation and Diagnosis: Single and Interconnected Systems. 2018, Springer International Publishing, Cham, 15–44.
158. Singh, S., Kumar, A., Panneerselvam, K., Vennila, J.J., Diagnosis of arthritis through fuzzy inference system. J. Med. Syst. 36 (2012), 1459–1468.
159. Nhivekar, G.S., Nirmale, S.S., Mudholkar, R.R., A survey of fuzzy logic tools for fuzzy-based system. Proceedings of International Conference in Recent Trends in Information Technology and Computer Science (ICRTITCS-2012), 2012.