A Decision Support System for Predicting Heart Disease Using Multilayer Perceptron and Factor Analysis
(*) Corresponding author
In the most recent decades, several tools and various methodologies have been proposed by the researchers for developing effective medical decision support systems. The medical diagnosis by nature is a complex and fuzzy cognitive process, and soft computing methods, such as neural networks, have indicated extraordinary potential to be applied in the development of the medical decision support systems (MDSS). Diagnosing of coronary disease is one of the critical issues to develop medical decision support system which will help the doctors to take viable decisions. Disease diagnosis can be solved by classification which is one of the vital techniques of Data mining. Neural Network has risen as an important tool for classification. In this paper, a multilayer perceptron based decision support system is developed to support the diagnosis of heart diseases. For diagnosis of heart disease significantly 13 attributes are used for this purpose. 95% classification accuracy has been obtained from the experiments performed on the data taken from heart disease database. The outcomes acquired shows that the designed diagnostic system is capable of predicting the risk level of heart disease effectively.
Copyright © 2015 Praise Worthy Prize - All rights reserved.
Hongmei Yan, Yingtao Jiang, Jun Zheng, Chenglin Peng, Qinghui Li,” A multilayer perceptron-based medical decision support system for heart disease diagnosis”, Expert Systems with Applications 30 (2006) 272–281.
Sunila, Prabhat Panday, Nirmal Godara,” Decision Support System for Cardiovascular Heart Disease Diagnosis using Improved Multilayer Perceptron”, International Journal of Computer Applications, ISSN: 0975 – 8887, Volume 45– No.8, May 2012,pp:12-20.
Sellappan Palaniappan, Rafiah Awang, "Intelligent Heart Disease Prediction System Using Data Mining Techniques", IJCSNS International Journal of Computer Science and Network Security, Vol.8 No.8, August 2008.
A. T. Sayad, P. P. Halkarnikar,” Diagnosis of Heart Disease Using Neural Network Approach”, International Journal of Advances in Science Engineering and Technology, ISSN: 2321-9009, Volume- 2, Issue-3, July-2014,pp:88-92.
Resul Das,Ibrahim Turkoglu , Abdulkadir Sengur,” Effective diagnosis of heart disease through neural networks ensembles”, Expert Systems with Applications 36 (2009) ,ISSN:7675–7680,pp:7676-7680.
Bishop, Christopher M. (1995). Neural networks for pattern recognition. Oxford University Press.
Manda R.Narasinga Rao ,G.R.Sridhar ,K.Madhu,Allam Appa Rao “A clinical decision support system using multilayer perceptron neural network to predict wellbeing in diabetes” , Journal of Association of Physicians of India, February,2009, Pg.No:127-33.
Sunila Godara, Nirmal,” Intelligent and Effective Decision Support System Using Multilayer Perceptron”, International Journal of Engineering Research and Applications(IJERA), ISSN: 2248-9622, Vol. 1, Issue 3, pp.513-518.
Yanwei, X.; Wang, J.; Zhao, Z.; and Gao, Y, “Combination data mining models with new medical data to predict outcome of coronary heart disease”, Proceedings International Conference on Convergence Information Technology 2007, p 868 – 872.
Colombet, I.; Ruelland, A.; Chatellier, G.; Gueyffier, F. (2000). “Models to predict cardiovascular risk: comparison of CART, multilayer perceptron and logistic regression”. Proceedings of AMIA Symp 2000, p 156-160.
Khemphila, A.; Boonjing, V. (2010). “Comparing performance of logistic regression, decision trees and neural networks for classifying heart disease patients”. Proceedings of International Conference on Computer Information System and Industrial Management Applications 2010, p 193 – 198.
An Gie Yong, Sean Pearce,” A Beginner’s Guide to Factor Analysis: Focusing on Exploratory Factor Analysis”, Tutorials in Quantitative Methods for Psychology 2013, Vol. 9(2), p. 79-94.
Hicham, A., Bouhorma, M., Abdellah, E., Integration of Fuzzy Delphi, Fuzzy Clustering and Back-Propagation Neural Networks with Adaptive Learning Rate for Sales Forecasting in ERP Architecture, (2013) International Journal on Information Technology (IREIT), 1 (1), pp. 11-21.
Soedibyo, Stephani, R., Aprilely, A.F., Ratih, M.S., Primaditya, S., Suyanto, Power optimization for adaptive wind turbine: Case study on islanded and grid connected, (2014) International Review of Electrical Engineering (IREE), 9 (4), pp. 835-843.
Hasan, M.H., Al Hazza, M.H.F., Tensile parameters evaluation of two solid solution super alloys by ANN modeling, (2014) International Review of Mechanical Engineering (IREME), 8 (2), pp. 338-343.
Bouchiba, F., Nouibat, W., Neuro-fuzzy navigation of a mobile robot in an unknown environment, (2015) International Review of Automatic Control (IREACO), 8 (3), pp. 220-227.
Farahat, M.A., Abd Elgawed, A.F., Mustafa, H.M.M., Ibrahim, A., Short term load forecasting using BP neural network optimized by particle swarm optimization, (2013) International Review on Modelling and Simulations (IREMOS), 6 (2), pp. 450-454.
- There are currently no refbacks.
Please send any question about this web site to email@example.com
Copyright © 2005-2023 Praise Worthy Prize