Abstract

This paper presents an application of Artificial Neural Networks (ANN) in predicting patient coronary heart disease status. Multilayer perceptron (MLP) which is a type of ANN architecture was used to develop the proposed model. Several experiments were carried out to determine the network optimal parameters. Overall, the optimised ANN system achieved a very high diagnosing accuracy of 92.2%, proving its usefulness in support of diagnosis process of coronary heart disease.

References

• NHS (2011) Coronary heart disease. Available at:http://www.nhs.uk/conditions/coronary-heart-disease/Pages/Introduction.aspx(Accessed: 22/12/11)
• Shi L and Wang XC. 2010. Artificial neural networks: Current applications in modern medicine. IEEE,3837
• Negnevitsky, M (2002) Artificial Intelligence: A guide to intelligent systems. 1st edition.pearson.
• Kurt, I., Ture, M. & Kurum, A. T, (2008).Comparing performances of logistic regression, classification, regression tree and neural networks for predicting coronary artery disease. Expert Systems with Applications, 34, 366–374.[online]. Available at: http://www.sciencedirect.com (Accessed: 21 March 2018]
• Sibanda, W and Pretorius, P.(2011). Novel Application of Multi-Layer Perceptrons (MLP) Neural Networks to Model HIV in South Africa using Seroprevalence Data from Antenatal Clinics. International Journal of Computer Applications, 35(2). Available at: www.ijcaonline.org (Accessed: 24 March 2018]
• Delen, D., Walker, G., & Kadam, A. (2005).Predicting breast cancer survivability: A comparison of three data mining methods. Artificial Intelligence in Medicine, 34(2), 113–127. Available at: http://www.sciencedirect.com (Accessed: 20 March 2018]
• Ozyilmaz, L.& Yildirim, T. (2003).Artificial neural networks for diagnosis of hepatitis disease. In: Proceedings of the International Joint Conference on Neural Networks, Vol 1, 586 – 589. July 20-24. Istanbul, Turkey
• Shanthi, D., Sahoo, .G, & Saravanan, N.(2008). Input Feature Selection using Hybrid Neuro-Genetic Approach in the Diagnosis of Stroke Disease. International Journal of Computer Science and Network Security 8(12).
• Junita M.S and Brian S.H.2008. Improved Neural Network Performance Using Principal Component Analysis on Matlab. International Journal of The Computer , The Internet and Management 16(2).pp1-8
• Hongmei, Y., Yingtao,J., Jun,Z., Chenglin,P. & Qingui, Lui (2006). A Multilayer perceptron-based Medical decision System for heart disease diagnosis . Expert Systems with Applications 30(2006),271-281.
• Khemphila,A. and Boonjink, V. (2010) Comparing performances of logistic regression, decision trees , and neural networks for classifying heart disease patients.In:CISIM,2010 International conference on computer information systems and industrial management application. Krackow 8-10 Oct. 2010
• Mutasem Khalil,S.A, Khairuddin, B,O. and Shahrul, A.N, (2009). Back Propagation Algorithm: The Best Algorithm Among the Multi-layer Perceptron Algorithm. International Journal of Computer Science and Network Security 9(4).
• Bernard, W., Aaron, G., and Youngsik, K & Dookum, P. (2013). The No-Prop algorithm: A new learning algorithm for the multlayer neural network. Neural Networks 37(2013) 182-188. Available at: www.elservier.com/locate/neunet. [Accessed: 02 September 2018]
• Kavzoĝlu, Taşkin (2001) An investigation of the design and use of feed-forward artificial neural networks in the classification of remotely sensed images. PhD thesis, University of Nottingham., viewed 15 October 2017 < http://eprints.nottingham.ac.uk/13872/>
• Attoh-Okine, N.O. (1999). Analysis of learning rate and momentum term in back propagation neural network algorithm trained to predict pavement performance, Advances in Engineering Software 30(4), p 291-302