Abstract
Sudden Cardiac Death (SCD) is a medical problem that is responsible for over 300,000 deaths per year in the United States and millions worldwide. SCD is defined as death occurring from within one hour of the onset of acute symptoms, an unwitnessed death in the absence of pre-existing progressive circulatory failures or other causes of deaths, or death during attempted resuscitation. Sudden death due to cardiac reasons is a leading cause of death among Congestive Heart Failure (CHF) patients. The use of Electronic Medical Records (EMR) systems has made a wealth of medical data available for research and analysis. Supervised machine learning methods have been successfully used for medical diagnosis. Ensemble classifiers are known to achieve better prediction accuracy than its constituent base classifiers. In an effort to understand the factors contributing to SCD, data on 2,521 patients were collected for the Sudden Cardiac Death in Heart Failure Trial (SCD-HeFT). The data included 96 features that were gathered over a period of 5 years. The goal of this work was to develop a model that could accurately predict SCD based on available features. The prediction model used the Cox proportional hazards model as a score and then used the ExtraTreesClassifier algorithm as a boosting mechanism to create the ensemble. We tested the system at prediction point of 180 days. Our best results were at 180-days with accuracy of 0.9624, specificity of 0.9915, and F1 score of 0.9607.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ebrahimzadeh, E., Pooyan, M., Bijar, A.: A novel approach to predict sudden cardiac death (SCD) using nonlinear and time-frequency analyses from HRV signals. PLoS ONE (2014). https://doi.org/10.1371/journal.pone.0081896
Murukesan, L., Murugappan, M., Iqbal, M.: Sudden cardiac death prediction using ECG signal derivative (Heart Rate Variability): a review. 2013 IEEE 9th international colloquium on signal processing and its applications. (2013) https://doi.org/10.1109/cspa.2013.6530054
Murukesan, L., Murugappan, M., Iqbal, M., Saravanan, K.: Machine learning approach for sudden cardiac arrest prediction based on optimal heart rate variability features. J. Med. Imaging Health Inf. 4, 521–532 (2014)
Ayesta, A., Martínez-Sellés, H., Bayés de Luna, A., Martínez-Sellés, M.: Prediction of sudden death in elderly patients with heart failure. J. Geriatr. Cardiol. JGC 15, 185–192 (2018)
Devi, R., Tyagi, H.K., Kumar, D.: Heart rate variability analysis for early stage prediction of sudden cardiac death. World Acad. Sci. Eng. Technol. 10, 432–435 (2016)
Deyell, M.W., Krahn, A.D., Goldberger, J.J.: Sudden cardiac death risk stratification. Circ. Res. 116, 1907–1918 (2015)
Omahony, C., Jichi, F., Pavlou, M., et al.: A novel clinical risk prediction model for sudden cardiac death in hypertrophic cardiomyopathy (HCM Risk-SCD). Eur. Heart J. 35, 2010–2020 (2013)
Pascual-Figal, D.A., Ordoñez-Llanos, J., Tornel, P.L., Vázquez, R., Puig, T., Valdés, M., Cinca, J., Luna, A.B.D., Bayes-Genis, A.: Soluble ST2 for predicting sudden cardiac death in patients with chronic heart failure and left ventricular systolic dysfunction. J. Am. Coll. Cardiol. 54, 2174–2179 (2009)
Ramírez, J., Orini, M., Mincholé, A., Monasterio, V., Cygankiewicz, I., Luna, A.B.D., Martínez, J.P., Pueyo, E., Laguna, P.: T-wave morphology restitution predicts sudden cardiac death in patients with chronic heart failure. J. Am. Heart Assoc. (2017). https://doi.org/10.1161/jaha.116.005310
Shiga, T., Kohro, T., Yamasaki, H., Aonuma, K., Suzuki, A., Ogawa, H., Hagiwara, N., Yamazaki, T., Nagai, R., Kasanuki, H.: Body mass index and sudden cardiac death in Japanese patients after acute myocardial infarction: data from the JCAD study and HIJAMI-II registry. J. Am. Heart Assoc. (2018). https://doi.org/10.1161/jaha.118.008633
Bardy, G.H., Lee, K.L., Mark, D.B., et al.: Amiodarone or an implantable cardioverter-defibrillator for congestive heart failure. N. Engl. J. Med. 352, 225–237 (2005)
Heart Failure. https://medlineplus.gov/heartfailure.html Accessed 4 Aug 2020
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning Data Mining, Inference, and Prediction, 2nd edn. Springer, New York (2009)
Dietterich, T.G.: Ensemble methods in machine learning. In: Multiple Classifier Systems Lecture Notes in Computer Science, pp.1–15. Springer, Berlin (2000)
Therneau, T., Crowson, C., Atkinson, E.: Using time dependent covariates and time dependent coefficients in the cox model. Survival Vignettes (2017)
Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63, 3–42 (2006)
Huikuri, H.V., Mäkikallio, T.H., Raatikainen, M.J.P., Perkiömäki, J., Castellanos, A., Myerburg, R.J.: Prediction of sudden cardiac death. Circulation 108, 110–115 (2003)
Shen, T.-W., Shen, H.-P., Lin, C.-H., Ou, Y.-L.: Detection and prediction of sudden cardiac death (SCD) for personal healthcare. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2007). https://doi.org/10.1109/iembs.2007.4352855
Piccini, J.P., Zhang, M., Pieper, K., Solomon, S.D., Al-Khatib, S.M., Werf, F.V.D., Pfeffer, M.A., Mcmurray, J.J., Califf, R.M., Velazquez, E.J.: Predictors of sudden cardiac death change with time after myocardial infarction: results from the VALIANT trial. Eur. Heart J. 31, 211–221 (2009)
Liew, R.: Electrocardiogram-based predictors of sudden cardiac death in patients with coronary artery disease. Clin. Cardiol. 34, 466–473 (2011)
Stecker, E.C., Chugh, S.S.: Prediction of sudden cardiac death: next steps in pursuit of effective methodology. J. Intervent. Card. Electrophysiol. 31, 101–107 (2011)
Vadakkumpadan, F., Trayanova, N., Younes, L., Wu, K.C.: Left-ventricular shape analysis for predicting sudden cardiac death risk. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2012). https://doi.org/10.1109/embc.2012.6346860
Shastri, S., Tangri, N., Tighiouart, H., et al.: Predictors of sudden cardiac death: a competing risk approach in the hemodialysis study. Clin. J. Am. Soc. Nephrol. 7, 123–130 (2011)
Sheela, C.J., Vanitha, L.: Prediction of sudden cardiac death using support vector machine. In: 2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014] (2014). https://doi.org/10.1109/iccpct.2014.7054771
Iglesias, D.G., Gutiérrez, N.R., Cos, F.D., Calvo, D.: Analysis of the high-frequency content in human QRS complexes by the continuous wavelet transform: an automatized analysis for the prediction of sudden cardiac death. Sensors 18, 560 (2018)
Vanitha, L., Suresh, G.R., Jenefarsheela, C.: Sudden cardiac death prediction system using hybrid classifier. In: 2014 International Conference on Electronics and Communication Systems (ICECS) (2014). https://doi.org/10.1109/ecs.2014.6892677
Fan, X., Hua, W., Xu, Y., Ding, L., Niu, H., Chen, K., Xu, B., Zhang, S.: Incidence and predictors of sudden cardiac death in patients with reduced left ventricular ejection fraction after myocardial infarction in an era of revascularisation. Heart 100, 1242–1249 (2014)
Wellens, H.J.J., Schwartz, P.J., Lindemans, F.W., et al.: Risk stratification for sudden cardiac death: current status and challenges for the future. Eur. Heart J. 35, 1642–1651 (2014)
Adabag, S., Rector, T.S., Anand, I.S., Mcmurray, J.J., Zile, M., Komajda, M., Mckelvie, R.S., Massie, B., Carson, P.E.: A prediction model for sudden cardiac death in patients with heart failure and preserved ejection fraction. Eur. J. Heart Fail. 16, 1175–1182 (2014)
Deo, R., Norby, F.L., Katz, R., et al.: Development and validation of a sudden cardiac death prediction model for the general population. Circulation 134, 806–816 (2016)
Lee, H., Shin, S.-Y., Seo, M., Nam, G.-B., Joo, S.: Prediction of ventricular tachycardia one hour before occurrence using artificial neural networks. Sci. Rep. (2016). https://doi.org/10.1038/srep32390
Weeks, P.A., Sieg, A., Gass, J.A., Rajapreyar, I.: The role of pharmacotherapy in the prevention of sudden cardiac death in patients with heart failure. Heart Fail. Rev. 21, 415–431 (2016)
Ramírez, J., Orini, M., Pueyo, E., Laguna, P.: Comparison of ECG T-wave duration and morphology restitution markers for sudden cardiac death prediction in chronic heart failure. In: 2017 Computing in Cardiology Conference (CinC) (2017). https://doi.org/10.22489/cinc.2017.224-267
Desai, M.Y., Smedira, N.G., Dhillon, A., Masri, A., Wazni, O., Kanj, M., Sato, K., Thamilarasan, M., Popovic, Z.B., Lever, H.M.: Prediction of sudden death risk in obstructive hypertrophic cardiomyopathy: potential for refinement of current criteria. J. Thorac. Cardiovasc. Surg. (2018). https://doi.org/10.1016/j.jtcvs.2018.03.150
Hypertrophic cardiomyopathy. In: Mayo Clinic. https://www.mayoclinic.org/diseases-conditions/hypertrophic-cardiomyopathy/symptoms-causes/syc-20350198 (2020). Accessed 15 Aug 2020
Mohanty, M., Sahoo, S., Biswal, P., Sabut, S.: Efficient classification of ventricular arrhythmias using feature selection and C4.5 classifier. Biomed. Signal Process. Control 44, 200–208 (2018)
Su, Y., Xia, M., Cao, J., Gao, Q.: Cardiac characteristics in the premature ventricular contraction patients with or without ventricular tachycardia. J. Clin. Exp. Med. 11, 6106–6112 (2018)
Ng, G.A., Mistry, A., Li, X., Schlindwein, F.S., Nicolson, W.B.: LifeMap: towards the development of a new technology in sudden cardiac death risk stratification for clinical use. EP Eur. (2018). https://doi.org/10.1093/europace/euy080
Rosset, S., Domingo, A.M., Asimaki, A., Graf, D., Metzger, J., Schwitter, J., Rotman, S., Pruvot, E.: Reduced desmoplakin immunofluorescence signal in arrhythmogenic cardiomyopathy with epicardial right ventricular outflow tract tachycardia. HeartRhythm Case Rep. 5, 57–62 (2019)
Thomsen, M.B., Nielsen, M.S., Aarup, A., Bisgaard, L.S., Pedersen, T.X.: Uremia increases QRS duration after β-adrenergic stimulation in mice. Physiol. Rep. (2018). https://doi.org/10.14814/phy2.13720
Özyılmaz, S., Satılmışoğlu, M.H., Gül, M., Uyarel, H., Serdar, O.A.: Assessment of the association between serum uric acid level and the predicted risk score of sudden cardiac death at five years in patients with hypertrophic cardiomyopathy. Turk Kardiyol. Dern. Ars. 46, 111–120 (2018)
Ebrahimzadeh, E., Fayaz, F., Ahmadi, F., Dolatabad, M.R.: Linear and nonlinear analyses for detection of sudden cardiac death (SCD) using ECG and HRV signals. Trends Res. (2018). https://doi.org/10.15761/tr.1000105
Harrington, P.: Machine Learning in Action. Manning, Shelter Island (2012)
Valentini, G., Masulli, F.: Ensembles of learning machines. In: Neural Nets Lecture Notes in Computer Science, pp. 3–20. Springer, Berlin (2002)
Johansson, R., Bostrom, H., Karlsson, A.: A study on class-specifically discounted belief for ensemble classifiers. In: 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (2008). https://doi.org/10.1109/mfi.2008.4648012
Ramos-Jiménez, G., Campo-Ávila, J.D., Morales-Bueno, R.: Hybridizing ensemble classifiers with individual classifiers. In: 2009 Ninth International Conference on Intelligent Systems Design and Applications (2009). https://doi.org/10.1109/isda.2009.148
Bagheri, M.A., Gao, Q.: An efficient ensemble classification method based on novel classifier selection technique. In: Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics - WIMS 12 (2012). https://doi.org/10.1145/2254129.2254157
Verma, B., Rahman, A.: Cluster-oriented ensemble classifier: impact of multicluster characterization on ensemble classifier learning. IEEE Trans. Knowl. Data Eng. 24, 605–618 (2012)
Gupta, R., Audhkhasi, K., Narayanan, S.: Training ensemble of diverse classifiers on feature subsets. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2014). https://doi.org/10.1109/icassp.2014.6854136
Yu, Z., Li, L., Liu, J., Han, G.: Hybrid adaptive classifier ensemble. IEEE Trans. Cybern. 45, 177–190 (2015)
Dijk, M.R.V., Steyerberg, E.W., Stenning, S.P., Dusseldorp, E., Habbema, J.D.F.: Survival of patients with nonseminomatous germ cell cancer: a review of the IGCC classification by Cox regression and recursive partitioning. Br. J. Cancer 90, 1176–1183 (2004)
Zhao, J.: Mixed-effects cox models of alcohol dependence in extended families. BMC Genet. (2005). https://doi.org/10.1186/1471-2156-6-s1-s127
Bellera, C.A., Macgrogan, G., Debled, M., Lara, C.T.D., Brouste, V., Mathoulin-Pélissier, S.: Variables with time-varying effects and the cox model: some statistical concepts illustrated with a prognostic factor study in breast cancer. BMC Med. Res. Methodol. (2010). https://doi.org/10.1186/1471-2288-10-20
Royston, P., Altman, D.G.: External validation of a Cox prognostic model: principles and methods. BMC Med. Res. Methodol. (2013). https://doi.org/10.1186/1471-2288-13-33
Darwiche, A., Mukherjee, S.: Machine learning methods for septic shock prediction. In: Proceedings of the 2018 International Conference on Artificial Intelligence and Virtual Reality - AIVR 2018 (2018). https://doi.org/10.1145/3293663.3293673
Klein, H., Auricchio, A., Reek, S., Geller, C.: New primary prevention trials of sudden cardiac death in patients with left ventricular dysfunction: SCD-HEFT and MADIT-II. Am. J. Cardiol. 83, 91–97 (1999)
Dziura, J.D., Post, L.A., Zhao, Q., Fu, Z., Peduzzi, P.: Strategies for dealing with missing data in clinical trials: from design to analysis. Yale J. Biol. Med. 86, 343–358 (2013)
Kotsiantis, S., Dimitris, D., Pintelas, P.: Handling imbalanced datasets: a review. GESTS Int. Trans. Comput. Sci. Eng. 30, 25–36 (2006)
Fox, J., Weisberg, S.: Cox proportional-hazards regression for survival data in R. An Appendix to an R Companion to Applied Regression, 2nd edn. (2011)
Beleites, C., Neugebauer, U., Bocklitz, T., Krafft, C., Popp, J.: Sample size planning for classification models. Anal. Chim. Acta 760, 25–33 (2013)
Refaeilzadeh, P., Tang, L., Liu, H.: Cross-validation. Encycl. Database Syst. 5, 532–538 (2009)
Kursa, M.B., Rudnicki, W.R.: Feature selection with the Boruta package. J. Stat. Softw. 36, 1–13 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
El-Geneidy, A., Mukherjee, S., Darwiche, A. (2021). Prediction of Sudden Cardiac Death Using Ensemble Classifiers. In: Arai, K. (eds) Advances in Information and Communication. FICC 2021. Advances in Intelligent Systems and Computing, vol 1364. Springer, Cham. https://doi.org/10.1007/978-3-030-73103-8_48
Download citation
DOI: https://doi.org/10.1007/978-3-030-73103-8_48
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-73102-1
Online ISBN: 978-3-030-73103-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)