Part of the book series:Lecture Notes in Computer Science ((LNAI,volume 1211))
Included in the following conference series:
141Accesses
Abstract
Using only non invasive medical information, we propose inductive decision trees exploiting C4.5 algorithm, artificial neural networks with three MLP models, and linear discriminant analysis to diagnose coronary heart disease. The first neural network model is a constructive MLP called OIL (Orthogonal Incrementing Learning) that builds its hidden neurons during the training phase. The second one is a fixed MLP architecture with the same number of hidden neurons obtained from the first network building methodology. The last one is a special ”interpretable” MLP model with a fixed architecture (IMLP), which is interpretable through symbolic rule extraction. In general, explanation of connectionist model responses are difficult to obtain, especially when input examples have continuous variables. This is not acceptable for real world diagnosis applications. The novelty in our study consists in the interpretability of the IMLP model we have developed. For this diagnosis application, all neural networks globally obtain better predictive accuracies than C4.5 and the linear discriminant analysis. Results obtained with the OIL method are slightly better than those obtained by IMLP, but they lack interpretability.
This is a preview of subscription content,log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
White H. Connectionist non-parametric regression. multi-layer feedforward networks can learn arbitrary mappings. Neural Networks 1990: 3 (3); 535–551.
Towell G.G, Shawlik J.W. Extracting Refined Rules from Knowledge-Based Neural Networks, Machine Learning, 13 (1), 1993.
Setiono R, Liu H. Understanding Neural Networks via Rule Extraction. IJCAI 1995: 1; 480–485.
Gorman R.P, Sejnowski T.J., Analysis of Hidden Unites in a Layered Network Trained to Classify Sonar Targets. Neural Networks 1988: 1(1); 75–88.
Andrews R, Geva S. Extracting Rules From a Constrained Error Backpropagation Network, Proc of the 5th Australian Conference on Neural Networks, Brisbane, 1994.
Mooney R, Shavlik J, Towell G, Gove A. An experimental Comparison of Symbolic and Connectionist Learning Algorithms, Proc. IJCAI-89 Morgan Kaufmann Los Altos, CA 775–780, 1989.
Atlas L, Cole R, Connor J, El-Sharkawi M, Marks R.J, Muthusumi Y, Barnard E. Performance Comparison Between Backpropagation Networks and Classification Trees on Three Real-World Applications, Touretzky (ed) Advances in Neural Information Processing 2, Morgan Kaufmann, San Mateo, CA, 622–629. 1990.
Tsoi A.C, Pearson R.A. Comparison of Three Classification Techniques, CART, C4.5 and MLP, Lippman R.P. et al (eds) Advances in Neural Information Processing 3, Morgan Kaufmann, San Mateo CA 963–969.
Mitchell T.M, Thsun S.B. Explanation Based Learning. A comparison of symbolic and connectionist Learning Algorithms, Proc 10th Int. Conf. on Machine Learning, Morgan Kaufmann San Mateo CA 197–204, 1993
Feng G, Sutherland A, King R, Muggleton S, Henery R. Comparison of Machine Learning Classifiers to Statistics and Neural Networks, Proc. 4th Int. Workshop on Artificial Intelligence and Statistics, Florida 1993.
Quinlan J.R. Comparing Connectionist and Symbolic Learning Methods, Hanson et al, 445–456, 1994.
Quinlan JR. C4.5: Programs for Machine Learning. Morgan Kaufmann 1993.
Amendolia S.R, Bertolucci E, Biadi O, Bottigli U, Caravelli P, Fantacci M.E, Fidecaro E, Mariani M, Messineo A, Rosso V, Stefanini A. Neural Network Expert System for Screening Coronary Heart Disease. Physica Medica 1993: IX (1); 13–17.
Fahlman S. E, Lebiere C. The Cascade-Correlation Learning Architecture TechReport 1990: CMU-CS-90-100Carnegie Mellon University
Lengellé R, Denoeux T, Training MLPs Layer by Layer Using an Objective Function for Internal Representations. Neural Networks 1996 vol:9 Nbr: 1; 83–98.
Vysniauskas V, Groen F.C, Krose J.A. Orthogonal Incremental Learning of a feedforward Network. ICANN'95, Paris; vol:1; p311.
Møllerr M., A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning. Neural Networks 1993 vol: 6 nbr: 4; 525–533
Riedmiller M. Rprop—Description and Implementation Details. TechReport riedmiller-94a. 1994
Huang HH, Zhang C, Lee S. Implementation and Comparison of Neural Network Learning Paradigms: Back Propagation, Simulated Annealing and Tabu Search. Artificial Neural Networks in Engineering 1991: ASME Press, New York; 95–100.
Bologna G, Pellegrini C. Three Medical Examples in Neural Network Rule Extraction (Submitted on 1996) to Physica Medica, ed. Giardini Editori e Stampatori in Pisa.
Delogu P. Uso di Reti Neurali per Diagnosi Cliniche Automatiche. Master Thesis, University of Pisa (Italy), 1996.
Author information
Authors and Affiliations
Artificial intelligence group, Computing Science Center, University of Geneva, 24 rue General Dufour, CH-1211, Geneva 4, Switzerland
Guido Bologna, Ahmed Rida & Christian Pellegrini
- Guido Bologna
You can also search for this author inPubMed Google Scholar
- Ahmed Rida
You can also search for this author inPubMed Google Scholar
- Christian Pellegrini
You can also search for this author inPubMed Google Scholar
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bologna, G., Rida, A., Pellegrini, C. (1997). Intelligent assistance for coronary heart disease diagnosis: A comparison study. In: Keravnou, E., Garbay, C., Baud, R., Wyatt, J. (eds) Artificial Intelligence in Medicine. AIME 1997. Lecture Notes in Computer Science, vol 1211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0029452
Download citation
Published:
Publisher Name:Springer, Berlin, Heidelberg
Print ISBN:978-3-540-62709-8
Online ISBN:978-3-540-68448-0
eBook Packages:Springer Book Archive
Share this paper
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative