Abstract
Coronary Artery Disease (CAD) is one of the leading causes of morbidity and mortality worldwide including India. Although recent advances in modern medical science have led to better diagnosis and treatment of CAD, yet its early detection is still a challenge. Fuzzy classification approaches are used to deal with uncertainty inherent in medical field. These fuzzy rule-based systems are extremely effective tools in disease diagnosis as they are capable to develop potential linguistic models. The aim of this paper is to initially develop a fuzzy rule-based classification system (FRBCS) based on clinical and epidemiological variables of patients and then to determine its accuracy in the diagnosis of CAD. The membership functions for medical attributes were chosen after extensive review of related literature. The rules were formulated as per the opinion of expert physicians. The present work describes the risk factors accountable for CAD, fuzzy modeling of clinical variables, rule evaluation and defuzzification of the fuzzified outputs to crisp values. The accuracy of the proposed fuzzy if–then rule classification system is 89%. Further, the present approach can assist medical practitioners in diagnosing CAD more precisely based on the fuzzy rules.
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References
Alizadehsani R, Zangooei MH, Hosseini MJ, Habibi J, Khosravi A, Roshanzamir M, Khozeimeh F, Sarrafzadegan N, Nahavandi S (2016) Coronary artery disease detection using computational intelligence methods. Knowl-Based Syst 109:187–197
Alizadehsani R, Hosseini MJ, Khosravi A, Khozeimeh F, Roshanzamir M, Sarrafzadegan N, Nahavandi S (2018) Non-invasive detection of coronary artery disease in high-risk patients based on the stenosis prediction of separate coronary arteries. Comput Methods Programs Biomed 162:119–127
Anooj PK (2012) Clinical decision support system: risk level prediction of heart disease using weighted fuzzy rules. J King Saud Univ—Comput Inf Sci 24:27–40
D’Acierno A, Esposito M, De Pietro G (2013) An extensible six-step methodology to automatically generate fuzzy DSSs for diagnostic applications. BMC Bioinformatics 14:S4
Dheeru D, Karra Taniskidou E (2017) UCI machine learning repository. https://archive.ics.uci.edu/ml
Mohammadpour RA, Abedi SM, Bagheri S, Ghaemian A (2015) Fuzzy rule-based classification system for assessing coronary artery disease. Comput Math Methods Med 2015:1–8
Muthukaruppan S, Er MJ (2012) A hybrid particle swarm optimization based fuzzy expert system for the diagnosis of coronary artery disease. Expert Syst Appl 39:11657–11665
Nazari S, Fallah M, Kazemipoor H, Salehipour A (2018) A fuzzy inference- fuzzy analytic hierarchy process-based clinical decision support system for diagnosis of heart diseases. Expert Syst Appl 95:261–271
Pal D, Mandana KM, Pal S, Sarkar D, Chakraborty C (2012) Fuzzy expert system approach for coronary artery disease screening using clinical parameters. Knowl-Based Syst 36:162–174
Paul AK, Shill PC, Rabin MRI, Murase K (2018) Adaptive weighted fuzzy rule-based system for the risk level assessment of heart disease. Appl Intell 48:1739–1756. https://doi.org/10.1007/s10489-017-1037-6
Peña-Reyes CA, Sipper M (2002) Combining evolutionary and fuzzy techniques in medical diagnosis. In: Schmitt M, Teodorescu H, Jain A, Jain A, Jain S, Jain LC (eds) Computational intelligence processing in medical diagnosis. Studies in fuzziness and soft computing. Physica, Heidelberg, pp 391–426
Priyatharshini R, Chitrakala S (2019) A self-learning fuzzy rule-based system for risk-level assessment of coronary heart disease. IETE J Res 65:288–297. https://doi.org/10.1080/03772063.2018.1431062
Reddy GT, Khare N (2017) An efficient system for heart disease prediction using hybrid OFBAT with rule-based fuzzy logic model. J Circuits, Syst Comput 26:1750061. https://doi.org/10.1142/S021812661750061X
Ross TJ (2010) Fuzzy logic with engineering applications. Wiley, Chichester, UK
Sabahi F (2018) Bimodal fuzzy analytic hierarchy process (BFAHP) for coronary heart disease risk assessment. J Biomed Inform 83:204–216
Sanz JA, Galar M, Jurio A, Brugos A, Pagola M, Bustince H (2014) Medical diagnosis of cardiovascular diseases using an interval-valued fuzzy rule-based classification system. Appl Soft Comput 20:103–111
Singh N, Singh P (2017) Rule based approach for prediction of chronic kidney disease: a comparative study. Biomed Pharmacol J 10:867–874
Singh N, Singh P (2019) Cardiac arrhythmia classification using machine learning techniques. In: Engineering vibration, communication and information processing. Springer, Singapore. pp 469–480
Tsipouras MG, Exarchos TP, Fotiadis DI, Kotsia AP, Vakalis KV, Naka KK, Michalis LK (2008) Automated diagnosis of coronary artery disease based on data mining and fuzzy modeling. IEEE Trans Inf Technol Biomed 12:447–458
Verma L, Srivastava S, Negi PC (2016) A hybrid data mining model to predict coronary artery disease cases using non-invasive clinical data. J Med Syst 40:178
Verma L, Srivastava S, Negi PC (2018) An intelligent noninvasive model for coronary artery disease detection. Complex Intell Syst 4:11–18
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Singh, N., Singh, P. (2021). Medical Diagnosis of Coronary Artery Disease Using Fuzzy Rule-Based Classification Approach. In: Rizvanov, A.A., Singh, B.K., Ganasala, P. (eds) Advances in Biomedical Engineering and Technology. Lecture Notes in Bioengineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-6329-4_27
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DOI: https://doi.org/10.1007/978-981-15-6329-4_27
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