International Journal of Advanced Networking and Applications

Open Access Open Access  Restricted Access Subscription Access

Medical Applications on Fuzzy Logic Inference System:A Review


Affiliations
1 Department of CS & IT, DAV College, Amritsar-143001, India
2 Sant Baba Bhag Singh University, Jalandhar-144030, India
 

Traditional approaches based on algorithms are not feasible for solving detection of diseases throughout the world. Fuzzy Logic becomes more and more popular for diagnosing diseases based on different parameters and methodologies. This paper represents the literature review of articles in fuzzy logic to deal with various medical applications for last decade (2008-2018)because of identified different methodologies developed in this time-period. On the basis of different fuzzy applications in medical field, this article focused on eight common medical issues like heart disease, asthma disease, liver disease, breast cancer, Parkinson disease, cholera, dental and diabetes disease. Based on these different medical applications, the basic objective is to explore and implement fuzzy logic in existing and different domains in future.

Keywords

ANFIS, Back-Propagation, Fuzzy Logic, Perceptron, Neuro-Fuzzy System.
User
Notifications
Font Size

  • Sengur, A. “An expert system based on linear discriminant analysis and adaptive neuro-fuzzy inference system to diagnosis heart valve diseases”, Expert Systems with Applications 2008; 35 (1–2): 214–222.

  • . Anbarasi, M., Anupriya, E., Iyengar, N. “Enhanced Prediction of Heart Disease with feature subset selection using Genetic Algorithm”. International Journal of Engineering Science and Technology 2010; 2(10):5370-5376.

  • Soni, J., Ansari, U., Sharma, D., Soni, S. “Predictive data mining for medical diagnosis: An overview of heart disease prediction”. International Journal of Computer Applications 2011; 17(8):43-48.

  • . Ansari, A.Q, Gupta, N.K. “Automated diagnosis of coronary heart disease using neuro fuzzy integrated system”. IEEE 2011. DOI:10.1109/wict.2011.6141450.

  • Ephzibah, E.P, Sundarapandian, V. “An expert system for heart disease diagnosis using neuro-fuzzy technique”. International Journal on Soft Computing, Artificial Intelligence and Applications 2012; 1(1).

  • . Olaniyi, E.O., Oyedotun, O.K., Helwan, A., Andan, K. “Neural Network diagnosis of heart diseases”. 2016; DOI:10.1109/ICABME.2015.7323241.

  • . Chauhan, R., Jangade, R., Rekapally. “Classification model for prediction of heart disease”. Springer 2018; 707-714.

  • . Zarandi, M.H., Zolnoori, M., Moin, M., Heidarnejad, H. “A fuzzy rule based expert system for diagnosing asthma”. Transaction E: Industrial Engineering 2010; 17(2):129-142.

  • . Patra, S., Thakur, G.S.M. “A proposed neuro fuzzy model for adult asthma disease diagnosis”. Computer Science and Information Technology 2014; pp: 191-205. DOI:10.5121/csit.2013.3218.

  • Badnjevic, A., Cifrek, M., Koruga, D., Osmankovic, D. “Neuro-Fuzzy classification of asthma and chronic obstructive pulmonary disease”. BMC Medical Informatics and Decision Making 2015; 15(Suppl3): S1:1-9.

  • . Satarkar, S.L., Ali, M.S. “Fuzzy expert system for the diagnosis of common liver disease”. International Engineering Journal for Research and Development 2013, 1(1).

  • . Hashmi, A., Khan, M.S. “Diagnosis blood test for liver disease using fuzzy logic”. International Journal of Sciences: Basic and Applied Research 2015; 20(1):151-183.

  • . Gallardo, J., Hernandez-Vera, B., Lasserre, A. “Interpretation of Mammographic using Fuzzy logic for early diagnosis of breast cancer”. IEEE Computer Society 2008; 278-283. DOI:10.1109/MICAI.2008.58.

  • . Adeli, M., Zarabadipour, H. “Automatic disease diagnosis systems using pattern recognition based genetic algorithm and neural networks”. International Journal of Physical Sciences 2011; 6(25):6076-6081.

  • . Sizilio, G., Leite, C., Guerreiro, A., Neto, A.D. “Fuzzy method for pre-diagnosis of breast cancer from the fine needle aspirate analysis”. BioMedical Engineering Online 2012;11(83):1-21.

  • . Sagir, A.M., Sathasivam, S. “Intelligence system based classification approach for medical disease diagnosis”. AIP Conference Proceedings 2017; DOI:10.1063/1.4995879.

  • . Geman, O. “Parkinson’s Disease assessment using Fuzzy expert system and non-linear dynamic”. Advances in Electrical and Computer Engineering 2013; 13(1): 41-46.

  • . Emokhare, B.O. and Igbape, E.M. “Fuzzy Logic based approach to early diagnosis of ebola hemorrhagic fever”. Proceedings of the World Congress on Engineering and Computer Science 2015; Vol II.

  • . Naskar S “Detection of Parkinson’s disease using neural network trained with genetic algorithm”. International Journal of Advance Research in Computer Science 2016; 7(5):46-51.

  • . Kaur, P., Trehan, H., Kaur, V., Dhilon, N. “Analysis of adaptive neuro-fuzzy based expert system for Parkinson’s Disease Diagnosis”. International Journal of Advanced Research, Ideas and Innovations in Technology 2017; 3(3): 1120-1127.

  • . Karunanithi, D. and Rodrigues, P. “Diagnosis of Parkinson’s Disease using Fuzzy Height”. International Journal of pure and applied mathematics 2018; 118(20): 4497-4502.

  • . Uduak, A., Mfon, M. “Proposed fuzzy framework for cholera diagnosis and monitoring”. International Journal of Computer Applications 2013; 82(17):1-10.

  • Okpor, M.D. “Using Fuzzy classifier for cholera analysis”. International Journal of Science and Research 2014; 3(8):314-317.

  • Allahverdi, N. and Akcan, T. “A fuzzy expert system design for diagnosis of Periodontal Dental Disease” IEEE 2011.

  • . Parewe, A.M., Mahmudy W.F., Ramdhani F., Anggodo, Y. “Dental disease detection using hybrid fuzzy logic and evolution strategies”. Journal of Telecommunications, Electronic and Computer Engineering; 10(10).

  • . Allahverdi. “Design of Fuzzy expert system and its applications in some medical areas”. International Journal of Applied Mathematics, Electronics and Computers 2014; 2(1):1-8.

  • . Ambara, B., Putra, D., Rusjayanthi, D. “Fuzzy expert system of oral and dental disease with certainity factor”. International Journal of Computer Science 2017, 14(3):22-30. DOI: 10.20943/01201703.2230.

  • . Polat, K., Gunes, S. “An expert system approach based on principal component analysis and adaptive neuro fuzzy inference system to diagnosis of diabetes disease”. Elsevier, Digital Signal Processing 2008; 17:702-710.

  • . Katigari, M.R, Ayatollahi, H., Malek, M. and Haghighi, M.K. “Fuzzy expert system for diagnosing diabetic neuropathy”, 2015. World J Diabetes, 8(2):80-88. doi:10.4239/wjd.v8.i2.80.


Abstract Views: 196

PDF Views: 0




  • Medical Applications on Fuzzy Logic Inference System:A Review

Abstract Views: 196  |  PDF Views: 0

Authors

Sunny Thukral
Department of CS & IT, DAV College, Amritsar-143001, India
Jatinder Singh Bal
Sant Baba Bhag Singh University, Jalandhar-144030, India

Abstract


Traditional approaches based on algorithms are not feasible for solving detection of diseases throughout the world. Fuzzy Logic becomes more and more popular for diagnosing diseases based on different parameters and methodologies. This paper represents the literature review of articles in fuzzy logic to deal with various medical applications for last decade (2008-2018)because of identified different methodologies developed in this time-period. On the basis of different fuzzy applications in medical field, this article focused on eight common medical issues like heart disease, asthma disease, liver disease, breast cancer, Parkinson disease, cholera, dental and diabetes disease. Based on these different medical applications, the basic objective is to explore and implement fuzzy logic in existing and different domains in future.

Keywords


ANFIS, Back-Propagation, Fuzzy Logic, Perceptron, Neuro-Fuzzy System.

References