Abstract
Cardiac arrhythmia refers to the medical condition during which the heart beats irregularly. Effective monitoring of cardiac patients can save enormous amount of lives. During the past few years, much importance has been gained by cardiac disease classification and prediction. This paper presents a model for diagnosis of cardiac arrhythmias. It works by selecting best features with the help of three filter-based feature selection methods on three different machine learning methods applied over cardiac arrhythmia dataset. Feature selection is a crucial preprocessing step in determining factors responsible for patients suffering from arrhythmia. In particular, we want to examine the underlying health factors of patients that could potentially be a powerful predictor for deaths that are related to heart. Three types of machine learning methods, namely, linear SVM, random forest, and JRip, were employed for analyzing the performance of the feature selection methods. Experimental analysis shows that highest accuracy of 85.58% was obtained with random forest classifier using gain ratio feature selection method with a subset of 30 features.
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References
Esteban, S., Tablado, M.R., Peper, F.E., Mahumud, Y.S., Ricci, R.I., Kopitowski, K.S., Terrasa, S.A.: Development and validation of various phenotyping algorithms for Diabetes Mellitus using data from electronic health records. Comput. Methods Programs Biomed. 152, 53–70 (2017)
Wang, Y., Li, P.-F., Tian, Y., Ren, J.-J., Li, J.-S.: A shared decision-making system for diabetes medication choice utilizing electronic health record data. IEEE J. Biomed. Heal. Informatics. 21, 1280–1287 (2017)
Heart Disease and Stroke | At A Glance Reports | Publications | Chronic Disease Prevention and Health Promotion | CDC, https://www.cdc.gov/chronicdisease/resources/publications/aag/heart-disease-stroke.htm
Andersson, C., Vasan, R.S.: Epidemiology of cardiovascular disease in young individuals. Nat. Rev. Cardiol. (2017)
Pławiak, P.: Novel methodology of cardiac health recognition based on ecg signals and evolutionary-neural system. Expert Syst. Appl. 92, 334–349 (2018)
Mehta, S.S., Lingayat, N.S.: Detection of P and T-waves in electrocardiogram. In: Proceedings of the World Congress on Engineering and Computer Science. pp. 22–24 (2008)
Bsoul, A.A.R., Ji, S.-Y., Ward, K., Najarian, K.: Detection of P, QRS, and T components of ECG using Wavelet Transformation. In: ICME International Conference on Complex Medical Engineering, 2009. CME, pp. 1–6 (2009)
Chariatis, A.: Very fast online learning of highly non linear problems. J. Mach. Learn. Res. 8, 2017–2045 (2007)
Canlas Jr., R.D.: Data Mining in Healthcare: Current Applications and Issues (2009)
Songthung, P., Sripanidkulchai, K.: Improving type 2 diabetes mellitus risk prediction using classification. In: 2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE), pp. 1–6 (2016)
Mohan, K.R., Paramasivam, I., Narayan, S.S.: Prediction and diagnosis of cardio vascular disease–a critical survey. In: 2014 World Congress on Computing and Communication Technologies (WCCCT), pp. 246–251 (2014)
Soman, T., Bobbie, P.O.: Classification of arrhythmia using machine learning techniques. WSEAS Trans. Comput. 4, 548–552 (2005)
Gupta, V., Srinivasan, S., Kudli, S.S.: Prediction and Classification of Cardiac Arrhythmia
Fazel, A., Algharbi, F., Haider, B.: Classification of Cardiac Arrhythmias Patients
Batra, A., Jawa, V.: Classification of arrhythmia using conjunction of machine learning algorithms and ECG diagnostic criteria. Int. J. Biol. Biomed. 1, 1–7 (2016)
Yeniterzi, S., Yeniterzi, R., Kücükural, A., Sezerman, U.: Feature selection with genetic algorithms on cardiac arrhythmia database. In: The 2nd International Symposium on Health Informatics and Bioinformatics (HIBIT) (2007)
Coast, D.A., Stern, R.M., Cano, G.G., Briller, S.A.: An approach to cardiac arrhythmia analysis using hidden Markov models. IEEE Trans. Biomed. Eng. 37, 826–836 (1990)
Luz, E.J.D.S., Schwartz, W.R., Cámara-Chávez, G., Menotti, D.: ECG-based heartbeat classification for arrhythmia detection: a survey. Comput. Methods Programs Biomed. 127, 144–164 (2016)
Jadhav, S.M., Nalbalwar, S.L., Ghatol, A.A.: ECG arrhythmia classification using modular neural network model. In: 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), pp. 62–66 (2010)
Hall, M.A., Smith, L.A.: Feature selection for machine learning: comparing a correlation-based filter approach to the wrapper. In: FLAIRS conference, AAAI. pp. 235–239., Florida, USA (1999)
Mitchell, T.M.: Machine Learning. McGraw Hill (1997)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer Science & Business Media (2013)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Cohen, W.W.: Fast effective rule induction. In: Proceedings of the Twelfth International Conference on Machine Learning, pp. 115–123 (1995)
Bache K., Lichman M.: UCI Machine Learning Repository. http://archive.ics.uci.edu/ml/
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software. SIGKDD Explor. Newsl. 11, 10 (2009)
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Singh, N., Singh, P. (2019). Cardiac Arrhythmia Classification Using Machine Learning Techniques. In: Ray, K., Sharan, S., Rawat, S., Jain, S., Srivastava, S., Bandyopadhyay, A. (eds) Engineering Vibration, Communication and Information Processing. Lecture Notes in Electrical Engineering, vol 478. Springer, Singapore. https://doi.org/10.1007/978-981-13-1642-5_42
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DOI: https://doi.org/10.1007/978-981-13-1642-5_42
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