Abstract
Cardiovascular diseases are the prominent causes of death each year. Data mining is an emerging area which has numerous applications specifically in healthcare. Our work suggests a system for predicting the risk of a cardiovascular disease using data mining techniques and is based on the ECG tests. It further recommends nearby relevant hospitals based on the prediction. We propose a multistage classification algorithm in which the first stage is used to classify normal and abnormal ECG beats and the next stage is used to refine the prediction done by the first stage by reducing the number of false negatives. In this work experiments have been conducted on the MIT-BIH Arrhythmia dataset which is a benchmark dataset. The results of the experiments show that the proposed technique is very promising.
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References
World Health Organisation. http://www.who.int/mediacentre/factsheets/fs317/en/
Qian, B., Wang, X., Cao, N., Li, H., Jian, Y.: A relative similarity based method for interactive patient risk prediction. Data Min. Knowl. Disc. 29(4), 1070–1093 (2015). Online first: (2014)
Vafaie, M.H., Ataei, M., Koofigar, H.R.: Heart diseases prediction based on ECG signals’ classification using a genetic-fuzzy system and dynamical model of ECG signals. Biomed. Signal Process. Control 14, 291–296 (2014)
Lee, J., McManus, D.D., Bourrell, P., Sörnmo, L., Chon, K.H.: Atrial flutter and atrial tachycardia detection using Bayesian approach with high resolution time–frequency spectrum from ECG recordings. Biomed. Signal Process. Control 8, 992–999 (2013)
Özbay, Y., Ceylan, R., Karlik, B.: Integration of type-2 fuzzy clustering and wavelet transform in a neural network based ECG classifier. Expert Syst. Appl. 38(1), 1004–1010 (2011)
Javadi, M., et al.: Classification of ECG arrhythmia by a modular neural network based on mixture of experts and negatively correlated learning. Biomed. Signal Process. Control 8(3), 289–296 (2013)
Wang, J.S., Chiang, W.C., Hsu, Y.L., Yang, Y.T.C.: ECG arrhythmia classification using a probabilistic neural network with a feature reduction method. Neurocomputing 116, 38–45 (2013)
Irigoyen, E., Miñano, G.: A NARX neural network model for enhancing cardiovascular rehabilitation therapies. Neurocomputing 109, 9–15 (2013)
Zidelmal, Z., Amirou, A., Ould-Abdeslam, D., Merckle, J.: ECG beat classification using a cost sensitive classifier. Comput. Methods Programs Biomed. 111(3), 570–577 (2013)
Fayn, J.: A classification tree approach for cardiac ischemia detection using spatiotemporal information from three standard ECG leads. IEEE Trans. Biomed. Eng. 58(1), 95–102 (2011)
Scalzo, F., Hamilton, R., Asgari, S., Kim, S., Hu, X.: Intracranial hypertension prediction using extremely randomized decision trees. Med. Eng. Phys. 34(8), 1058–1065 (2012)
Luz, E.J.D.S., Nunes, T.M., De Albuquerque, V.H.C., Papa, J.P., Menotti, D.: ECG arrhythmia classification based on optimum-path forest. Expert Syst. Appl. 40(9), 3561–3573 (2013)
Wu, Y., Zhang, L.: ECG classification using ICA features and support vector machines. In: Lu, B.-L., Zhang, L., Kwok, J. (eds.) ICONIP 2011, Part I. LNCS, vol. 7062, pp. 146–154. Springer, Heidelberg (2011)
Daamouche, A., Hamami, L., Alajlan, N., Melgani, F.: A wavelet optimization approach for ECG signal classification. Biomed. Signal Process. Control 7, 342–349 (2012)
Li, Q., Rajagopalan, C., Clifford, G.D.: A machine learning approach to multi-level ECG signal quality classification. Comput. Methods Programs Biomed. 117(3), 435–447 (2014)
Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Pang, C.-K., Stanley, H.E.: PhysioBank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000). (Circulation Electronic Pages; http://circ.ahajournals.org/cgi/content/full/101/23/e215)
Pantech Solutions. https://www.pantechsolutions.net/blog/what-is-biosignal/ecg-signal/
Acknowledgement
We are thankful to Dr. Ajay Bhargava (a cardiologist) for his guidance regarding the ECG features for disease prediction.
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Toshniwal, D., Goel, B., Sharma, H. (2015). Multistage Classification for Cardiovascular Disease Risk Prediction. In: Kumar, N., Bhatnagar, V. (eds) Big Data Analytics. BDA 2015. Lecture Notes in Computer Science(), vol 9498. Springer, Cham. https://doi.org/10.1007/978-3-319-27057-9_18
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DOI: https://doi.org/10.1007/978-3-319-27057-9_18
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