Abstract
The aim of this work is to detect whether an individual has a Cardio Vascular Disease (CVD) or not by using Mamdani and Sugeno methods of Fuzzy Inference System (FIS). The data set used for this work consists of 1000 records from the pathology reports of Thyrocare, Suburban Diagnostics, Medall, SRL Diagnostics and Metropolis. The parameters considered for predicting whether the individual has CVD or not are blood pressure, blood sugar, heart rate and oxygen level in the blood (SPO2). The FIS outputs indicate (1) whether the individual has a CVD or not, (2) the risk level of CVD and (3) some primary level precautions depending on the risk level. Mamdani and Sugeno methods were evaluated by comparing them with the results of the pathology reports. The results show that the Sugeno method gives 2% more accuracy in predicting a CVD as compared to the Mamdani method. Sugeno FIS gives more dynamical values as compared to Mamdani FIS for different values of input which leads to higher accuracy of Sugeno FIS.
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References
World Health Organization, Global Health Observatory Data. https://www.who.int/en/news-room/factsheets/. Last accessed 09 Feb 2020
Ordonez, C.: Association rule discover with the train and test approach for the heart disease prediction. IEEE Trans Inf Technol Biomed 10(2), 334–343 (2006)
Ali A., Mehdi N.: A fuzzy expert system for heart disease diagnosis. In: International Multi Conferences of Engineering and Computer Scientist, pp. 1–6. Hong Kong (2010)
Harun, U., Ahmet, A.: Detection of heart valve diseases by using fuzzy discrete hidden markov model. Elsevier J Expert Syst Appl 34(4), 2799–2811 (2008)
Oad, K.K., DeZhi, X., Butt, P.K.: A fuzzy rule based approach to predict risk level of heart disease. Glob. J. Comput. Sci. Technol. 14(3-C), 16–22 (2014)
Anuradha, R.P.G.: Design of rule based fuzzy expert system for diagnosis of cardiac diseases. In: National Conference on Innovative Trends in Science and Engineering 2016, vol. 4, pp. 313–320 (2016)
Kumar, A.V.S.: Diagnosis of heart disease using advanced fuzzy resolution mechanism. Int. J. Sci. Appl. Inf. Technol. 2(2), 22–30 (2013)
Neeru, P.R.: Implementation of fuzzy controller for diagnose of patient heart disease. Int. J. Innov. Sci. 2(4), 694–698 (2015)
Smita, S., Sushil, S.: Generic medical fuzzy expert system for diagnosis of cardiac diseases. Int. J. Comput. Appl. 66(13), 35–44 (2013)
Mathworks Homepage: Available from https://in.mathworks.com/matlab/.aspx. Last accessed 10 Mar 2020
Thyrocare Homepage: Available from https://www.thyrocare.com/Test_Menu.aspx. Last accessed 09 Feb 2020
Suburban Diagnostics Homepage: Available from https://www.suburbandiagnostics.com/pathology-tests/. Last accessed 09 Feb 2020
Medall Homepage: Available from https://www.medall.in/health.html. Last accessed 09 Feb 2020
SRL Diagnostics Homepage: Available from https://www.srlworld.com/health-packages/ahmedabad. Last accessed 09 Feb 2020
Metropolis Homepage: Available from https://www.metropolisindia.com/patients/labs-home/. Last accessed 09 Feb 2020
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Chaudhary, S., Gajjar, S., Bhowmick, P. (2021). Detection of Cardio Vascular Disease Using Fuzzy Logic. In: Senjyu, T., Mahalle, P.N., Perumal, T., Joshi, A. (eds) Information and Communication Technology for Intelligent Systems. ICTIS 2020. Smart Innovation, Systems and Technologies, vol 195. Springer, Singapore. https://doi.org/10.1007/978-981-15-7078-0_17
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DOI: https://doi.org/10.1007/978-981-15-7078-0_17
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