Abstract
Globally, cardiovascular (heart) diseases are the major cause of death. About 80% of deaths are reported in developing countries. Looking at the trend and lifestyle, one can predict that by 2030 around 23.6 million people may die due to heart disease (mainly from heart attacks and strokes). Each and every healthcare unit generates enormous heart disease data which unfortunately are not “mined” to discover pattern and knowledge for effective decision making. Practical knowledge by domain experts plays vital role. However, there is a need for effective analysis tools to discover unknown relationships and trends in data. Objective of this paper is to assess the accuracy of classification model for the prediction of heart disease for Cleveland dataset. A comparative study of parametric and nonparametric approach in classifying heart disease is presented. Two classification models, back-propagation neural network (BPNN) and logistic regression (LR), are used for the study. The developed classification model will assist domain experts to take effective diagnostic decision. 10-fold cross validation method is used to measure the unbiased estimate of these classification models.
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References
Dewan, A., Sharma, M.: Prediction of heart disease using a hybrid technique in data mining classification. IEEE (2015)
Palaniappan, S., Awang, R.: Intelligent heart disease prediction system using data mining techniques. In: IEEE (2008)
Berry, M.J.A., Linoff, G.: Data Mining Techniques. Wiley, New York (1997)
Bhatla, N., Jyoti, K.: An analysis of heart disease prediction using different data mining techniques. Int. J. Eng. Res. Technol. 1(8) (2012). ISSN: 2278-0181
Vijayarani, S., Sudha, S.: Comparative analysis of classification function techniques for heart disease prediction. Int. J. Innov. Res. Comput. Commun. Eng. 1(3) (2013)
Murthy, H.N., Meenakshi, M.: ANN model to predict coronary heart disease based on risk factors. Bonfring Int. J. Man Mach. Interface 3(2) (2013)
Artificial Neural Networks: https://doi.org/10.1007/978-3-319-43162-8_2
Patel, A.R., Joshi, M.M.: Heart diseases diagnosis using neural network. In: IEEE 31661 (2013)
Boger, Z., Guterman, H.: Knowledge extraction from artificial neural network models. In: IEEE Systems, Man, and Cybernetics Conference, Orlando, FL, USA (1997)
Blum, A.: Neural networks in C++. Wiley, New York (1992)
Sonawane, J.S., Patil, D.R.: Prediction of heart disease using multilayer perceptron neural network. In: IEEE (2014)
Yuan, J., Yu, S.: Privacy preserving back-propagation neural network learning made practical with cloud computing. IEEE Trans. Parallel Distrib. Syst. 25(1) (2014)
Gandhi, M., Singh, S.N.: Predictions in heart disease using techniques of data mining. In: International Conference on Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE). IEEE (2015)
Chaurasia, V., Pal, S.: Data mining approach to detect heart diseases. (2014)
Kirmani, M.M.: Heart disease prediction using multilayer perceptron algorithm. Int. J. 8(5) (2017)
Abdar, M., Zomorodi-Moghadam, M., Das, R., Ting, I.H.: Performance analysis of classification algorithms on early detection of liver disease. Expert Syst. Appl. 67, 239–251 (2017)
Amin, S.U., Agarwal, K., Beg, R.: Genetic neural network based data mining in prediction of heart disease using risk factors. In: IEEE Conference on Information and Communication Technologies (2013)
Sayad, A.T., Halkarnikar, P.P.: Diagnosis of heart disease using neural network approach. In: Proceedings of IRF International Conference (2014)
Giraddi, S., Pujari, J., Seeri, S.: Role of GLCM Features in Identifying Abnormalities in the Retinal Images. IJIGSP 7(6), 45–51 (2015). https://doi.org/10.5815/ijigsp.2015.06.06
Dua, D., Taniskidou, E.K.: UCI machine learning repository [http://archive.ics.uci.edu/ml]. University of California, School of Information and Computer Science, Irvine, CA (2017)
Acknowledgements
Authors acknowledge “UCI Machine Learning Repository” for heart disease dataset. This work is partially supported by KLE Tech University under “Capacity Building Projects” grants. Authors acknowledge KLE Society and KLE Tech University, Hubli, for providing funding and support.
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Desai, S.D., Giraddi, S., Narayankar, P., Pudakalakatti, N.R., Sulegaon, S. (2019). Back-Propagation Neural Network Versus Logistic Regression in Heart Disease Classification. In: Mandal, J., Bhattacharyya, D., Auluck, N. (eds) Advanced Computing and Communication Technologies. Advances in Intelligent Systems and Computing, vol 702. Springer, Singapore. https://doi.org/10.1007/978-981-13-0680-8_13
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DOI: https://doi.org/10.1007/978-981-13-0680-8_13
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