Intelligence Diagnostics of Heart Disease Based on Neural Networks Ensemble Use
DOI:
https://doi.org/10.18372/1990-5548.69.16420Keywords:
heart disease, neural networks ensemble, disease, electrocardiogram, echocardiography, Doppler examinationAbstract
The problem of constructing an intelligent system for diagnosing heart valve disease is considered. It is shown that the diagnosis is established on the basis of the results of a standard examination, which includes anamnesis, laboratory data, electrocardiogram, echocardiography and Doppler examination. The use of hybrid neural networks of ensemble topology is substantiated for solving the problem. In order to show that the appropriate neural networks for composing an ensemble can be effectively selected from a set of available neural networks, an algorithm for structural-parametric synthesis of hybrid neural networks of ensemble topology is proposed. As for training component neural networks it is used Bagging approach. Accuracy and diversity were used as criteria. The structure of the diagnostic system for recognition of valvular heart disease is presented. The results of research of the developed software are presented. As for training component neural networks, the Baging approach is used. The structure of the diagnostic system for recognition of valvular heart disease is presented. The results of the study of the developed software are presented.
References
http://yourtotalhealth.ivillage.com/heart-disease- fast-facts.html (accessed 13.02.08).
T. V. Ashcheulova, O. N. Kovaleva,N. A. Safargalina-Kornilova, N. N. Gerasimchuk, Priobretennyye poroki serdtsa: mitral'nyye i aortal'nyye: metod. ukaz. k prakt. zanyatiyam po propedevtike vnutr. meditsiny dlya studentov med. fak-tov vyssh. med. ucheb. zavedeniy III–IV urovnya akkreditatsii, Khar'kov: KHNMU, 2016, 28 s. [in Ukrainian]
I. A. Wright, N. A. J. Gough, F. Rakebrandt, M. Wahab, and J. P. Woodcock, "Neural network analysis of Doppler ultrasound blood flow signals: a pilot study," Ultrasound in Medicine & Biology, 23(5), 1997, pp. 683–690. https://doi.org/10.1016/S0301-5629(97)00011-2
B. C. B. Chan, F. H. Y. Chan, F. K. Lam, P. W. Lui, and P. W. F. Poon, "Fast detection of venous air embolism is Doppler heart sound using the wavelet transform," IEEE Transactions on Biomedical Engineering, 44 (4), 1997, pp. 237–245. https://doi.org/10.1109/10.563293
I. Guler, M. K. Kiymik, S. Kara, and M. E. Yuksel, "Application of autoregressive analysis to 20MHz pulsed Doppler data in real time," International Journal Biomedical Computing, 31(3–4), 1992, pp. 247–256. https://doi.org/10.1016/0020-7101(92)90008-G
Michael Z. Zgurovsky, Viktor M. Sineglazov, Olena I. Chumachenko, Artificial Intelligence Systems Based on Hybrid Neural Networks, Springer, 2020, https://link.springer.com/book/10.1007/978-3-030-48453-8. Customer can order it via https://www.springer.com/gp/book/9783030484521
P. Sollich and A. Krogh, Learning with ensembles: How over-fitting can be useful, in: D. S. Touretzky, M. C. Mozer, M. E. Hasselmo (Eds.), Advances in Neural Information Processing Systems 8, Denver, CO, MIT Press, Cambridge, MA, 1996, pp. 190–196.
L. K. Hansen and P. Salamon, "Neural network ensembles," IEEE Trans. Pattern Anal. Machine Intelligence, 12 (10), 1990, pp. 993–1001. https://doi.org/10.1109/34.58871
A. Sharkey (Ed.), Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems, Springer, London, 1999. https://doi.org/10.1007/978-1-4471-0793-4_1
S. Gutta and H. Wechsler, "Face recognition using hybrid classifier systems," in: Proc. ICNN-96, Washington, DC, IEEE Computer Society Press, Los Alamitos, CA, 1996, pp. 1017–1022.
F. J. Huang, Z.-H. Zhou, H.-J. Zhang, and T. H. Chen, "Pose invariant face recognition," in: Proc. 4th IEEE International Conference on Automatic Face and Gesture Recognition, Grenoble, France, IEEE Computer Society Press, Los Alamitos, CA, 2000, pp. 245–250.
H. Drucker, R. Schapire, and P. Simard, "Improving performance in neural networks using a boosting algorithm," in: S. J. Hanson, J. D. Cowan, C. L. Giles (Eds.), Advances in Neural Information Processing Systems 5, Denver, CO, Morgan Kaufmann, San Mateo, CA, 1993, pp. 42–49.
L. K. Hansen, L. Liisberg, and P. Salamon, "Ensemble methods for handwritten digit recognition," in: Proc. IEEE Workshop on Neural Networks for Signal Processing, Helsingoer, Denmark, IEEE Press., Piscataway, NJ, 1992, pp. 333–342.
J. Mao, "A case study on bagging, boosting and basic ensembles of neural networks for OCR," in: Proc. IJCNN-98, vol. 3, Anchorage, AK, IEEE Computer Society Press, Los Alamitos, CA, 1998, pp. 1828–1833.
K. J. Cherkauer, "Human expert level performance on a scientific image analysis task by a system using combined artificial neural networks," in: P. Chan,S. Stolfo, D. Wolpert (Eds.), Proc. AAAI-96 Workshop onIntegrating Multiple Learned Models for Improving and Scaling Machine Learning Algorithms, Portland, OR, AAAI Press, Menlo Park, CA, 1996, pp. 15–21.
P. Cunningham, J. Carney, and S. Jacob, "Stability problems with artificial neural networks and the ensemble solution," Artificial Intelligence in Medicine, 20(3), 2000, pp. 217–225. https://doi.org/10.1016/S0933-3657(00)00065-8
Z.-H. Zhou, Y. Jiang, Y.-B. Yang, and S.-F. Chen, "Lung cancer cell identification based on artificial neural network ensembles," Artificial Intelligence in Medicine, 24 (1), 2002, pp. 25–36. https://doi.org/10.1016/S0933-3657(01)00094-X
Y. Shimshoni and N. Intrator, "Classification of seismic signals by integrating ensembles of neural networks," IEEE Trans. Signal Process, 46 (5), 1998 pp. 1194–1201. https://doi.org/10.1109/78.668782
L. Breiman, "Bagging predictors," Machine Learning, 24 (2), 1996, pp. 123–140. https://doi.org/10.1007/BF00058655
B. Efron and R. Tibshirani, An Introduction to the Bootstrap, Chapman & Hall, New York, 1993. https://doi.org/10.1007/978-1-4899-4541-9
P. Domingos, "Why does bagging work? A bayesian account and its implications," In David Heckerman, Heikki Mannila, Daryl Pregibon, and Ramasamy Uthurusamy, editors, Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD-97), p. 155. AAAI Press, 1997.
Resul Dasa,∗, Ibrahim Turkoglub, Abdulkadir Sengurb, "Diagnosis of valvular heart disease through neural networks ensembles," Computer methods and programs in biomedicine, 93, 2009, pp. 185–191. https://doi.org/10.1016/j.cmpb.2008.09.005
R. Das, I. Turkoglu, A. Sengur, "Effective diagnosis of heart disease through neural networks ensembles," Expert Systems with Applications, 2008. https://doi.org/10.1016/j.eswa.2008.09.013.
Ye. Bodyanskiy and O. Rudenko, Artificial neural networks: architectures, training, applications, Kharkov: Teletech, 2004, p. 369. [in Ukrainian]
V. Golovko, Neural networks: training, organization and application, Moscow: IPRZhR, 2001. p. 256. (Series “Neurocomputers and their application”. Book 4). [in Russian]
Gonzalo Martınez-Muñoz, Daniel Hernandez-Lobato, and Alberto Suárez, "An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 2, February 2009, pp. 245–259. https://doi.org/10.1109/TPAMI.2008.78
Gonzalo Martınez-Muñoz, and Alberto Suárez. "Aggregation Ordering in Bagging," in Proc. of the IASTED International Conference on Artificial Intelligence and Applications, Acta Press, 2004, pp. 258– 263.
Downloads
Published
Issue
Section
License
Authors who publish with this journal agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
