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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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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