Coronary Heart Disease detection and diagnosis using Artificial Neural Network

  • Seema Rani Research Scholar, University College of Engineering, Punjabi University, Patiala, Punjab, India,
  • Rajbir Kaur Assistant Professor, University College of Engineering, Punjabi University, Patiala, Punjab, India
  • Amit Wason Principal, Ambala College of Engineering and Applied Research, Ambala, Haryana, India
Keywords: Artificial Neural Networks, Coronary Heart Disease, and Mean Square Error

Abstract

Every year we see that the expiry percentage increases as an outcome of heart syndrome. There are many factors which lead to this increase. Diagnosis of this disease should be perfect so as to reduce this increase; whereas, a misdiagnosis by the doctors or lack of knowledge in the patient may lead to an increase in expiry percentage. This blood coagulates totally lumps the veins, the heart muscle becomes “ravenous” for oxygen. Within a small duration of time, a decrease of heart muscle cells occurs, triggering lasting injury. This is cardiac disease. This paper considered various factors for the identification of heart disease, the problems, and the cures for the same. An intelligent system has been implemented which can be used to diagnose heart diseases. The developed system may be used to avoid the wrong judgment which is the major fault that may happen by physicians. For the implementation of the intelligent system, we have used the data of stat log for heart disease. The database consists of characteristics of persons being spotted for cardiac problems. The finding was used to approve a patient having cardiac disorder or not. We have used eleven different clinical datasets for this test. The collected data were used for three activities, first for the training of the network, second was validation followed by testing of network with data for which it was trained. The smart system was demonstrated on three different techniques, Feed Forward Back Propagation, Cascade Forward and Layer Recurrent model. While doing this study, we observed a performance of 74.55 and 87.935 for cascade forward model and Feed Forward with Back Propagation model respectively. From this experimentation about the identification of coronary heart syndrome, from the experiment it is revealed that the best prototype is Feed Forward with Back Propagation among all three tested prototypes.

References

[1] Haykin, S., & Network, N. (2004). A comprehensive foundation. Neural networks, 2(2004), 41.
[2] Chakraborty, R.C. (2010), Soft Computing-Fundamental of neural network, Aug 10, 2010.
[3] Shamsuddin, N., Mustafa, M. N., Husin, S., & Taib, M. N. (2005, September). Classification of heart sounds using a multilayer feed-forward neural network. In 2005 Asian Conference on Sensors and the International Conference on new Techniques in Pharmaceutical and Biomedical Research (pp. 87-90). IEEE.
[4] Khemphila, A., & Boonjing, V. (2010, October). Comparing performances of logistic regression, decision trees, and neural networks for classifying heart disease patients. In 2010 international conference on computer information systems and industrial management applications (CISIM) (pp. 193-198). IEEE.
[5] Obayya, M., & Abou-Chadi, F. (2008, November). Data fusion for heart diseases classification using multi-layer feed forward neural network. In 2008 International Conference on Computer Engineering & Systems (pp. 67-70). IEEE.
[6] Shamsuddin, N., & Taib, M. N. (2011, March). Diagnosis of heart diseases using nonlinear ARX model. In 2011 IEEE 7th International Colloquium on Signal Processing and its Applications (pp. 388-394). IEEE.
[7] Khemphila, A., & Boonjing, V. (2011, August). Heart disease classification using neural network and feature selection. In 2011 21st International Conference on Systems Engineering (pp. 406-409). IEEE.
[8] Kampouraki, A., Manis, G., & Nikou, C. (2008). Heartbeat time series classification with support vector machines. IEEE transactions on information technology in biomedicine, 13(4), 512-518.
[9] Babu, S., Vivek, E. M., Famina, K. P., Fida, K., Aswathi, P., Shanid, M., & Hena, M. (2017, April). Heart disease diagnosis using data mining technique. In 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA) (Vol. 1, pp. 750-753). IEEE. IEEE, 750-753
[10] Waghulde, N. P., & Patil, N. P. (2014). Genetic neural approach for heart disease prediction. International Journal of Advanced Computer Research, 4(3), 778.
[11] Verma, T., & Srivastava, R. K. (2015). Artificial Neural Networks based heart disease predictive Approach. International Journal of Application or Innovation in Engineering & Management, 4(3), 029-032.
[12] Londhe, Aarti, & Joglekar, Amol (2016). Analysis of the risk of heart disease using ANN, International Journal of Advanced Research in Computer Engineering & Technology, 5(3), 569-572.
[13] Verma, L., Srivastava, S., Negi, P. C. (2016), A hybrid data mining model to predict coronary artery disease cases using non-invasive clinical data. Journal of medical systems, 40(7), 178.
[14] Melillo, P., Izzo, R., Orrico, A., Scala, P., Attanasio, M., Mirra, M., & Pecchia, L. (2015). Automatic prediction of cardiovascular and cerebrovascular events using heart rate variability analysis. PloS one, 10(3), e0118504.
[15] Rojas-Domínguez, A., Padierna, L. C., Valadez, J. M. C., Puga-Soberanes, H. J., & Fraire, H. J. (2017). Optimal hyper-parameter tuning of SVM classifiers with application to medical diagnosis. IEEE Access, 6, 7164-7176.
[16] Subhadra, K., & Vikas, B. (2019), Neural Network Based Intelligent System for Predicting Heart Disease. International Journal of Innovative Technology and Exploring Engineering, 8(5), 484-487.
Published
2019-07-27
Section
Articles