1. bookVolume 1 (2017): Issue 4 (October 2017)
Journal Details
License
Format
Journal
eISSN
2564-615X
First Published
30 Jan 2017
Publication timeframe
4 times per year
Languages
English
access type Open Access

Classification of coronary artery disease data sets by using a deep neural network

Published Online: 27 Oct 2017
Volume & Issue: Volume 1 (2017) - Issue 4 (October 2017)
Page range: 271 - 277
Journal Details
License
Format
Journal
eISSN
2564-615X
First Published
30 Jan 2017
Publication timeframe
4 times per year
Languages
English
Abstract

In this study, a deep neural network classifier is proposed for the classification of coronary artery disease medical data sets. The proposed classifier is tested on reference CAD data sets from the literature and also compared with popular representative classification methods regarding its classification performance. Experimental results show that the deep neural network classifier offers much better accuracy, sensitivity and specificity rates when compared with other methods. The proposed method presents itself as an easily accessible and cost-effective alternative to currently existing methods used for the diagnosis of CAD and it can be applied for easily checking whether a given subject under examination has at least one occluded coronary artery or not.

1. Nahar J, Imam T, Tickle KS, Chen YPP. Computational intelligence for heart disease diagnosis: A medical knowledge driven approach. Expert Systems with Applications. 2013; 40(1): 96-104.10.1016/j.eswa.2012.07.032Search in Google Scholar

2. Bounhas M, Mellouli K, Prade H, Serrurier M. Possibilistic classifiers for numerical data. Soft Computing 2012; 17(5): 733-751.10.1007/s00500-012-0947-9Open DOISearch in Google Scholar

3. Baati K, Hamdani TM, Alimi AM. Hybrid naive possibilistic classifier for heart disease detection from heterogeneous medical data. Hybrid Intelligent Systems (HIS) 2013 13th International Conference 2013; 234-239.10.1109/HIS.2013.6920488Search in Google Scholar

4. Baati K, Hamdani TM, Alimi AM. A modified hybrid naive possibilistic classifier for heart disease detection from heterogeneous medical data. Soft Computing and Pattern Recognition (SoCPaR) 6th International Conference 2014; 353-358.10.1109/SOCPAR.2014.7008032Search in Google Scholar

5. Prez MA, Mrquez CY, Nieto OC, Yez IL, Cruz AJA. Collaborative learning based on associative models: Application to pattern classification in medical datasets. Computers in Human Behavior 2015; 51(Part B): 771-779.10.1016/j.chb.2014.11.091Search in Google Scholar

6. Singh K, Rong J, Batten L. Sharing sensitive medical data sets for research purposes - a case study. Data Science and Advanced Analytics (DSAA) 2014 International Conference 2014; 555-562.10.1109/DSAA.2014.7058126Search in Google Scholar

7. Anooj PK. Implementing decision tree fuzzy rules in clinical decision support system after comparing with fuzzy based and neural network based systems. IT Convergence and Security (ICITCS) 2013 International Conference 2013; 1-6.10.1109/ICITCS.2013.6717845Search in Google Scholar

8. Srinivas K, Rao GR, Govardhan A. Rough-fuzzy classifier: A system to predict the heart disease by blending two difierent set theories. Arabian Journal for Science and Engineering 2014; 39(4): 2857-2868.10.1007/s13369-013-0934-1Search in Google Scholar

9. Zhang B, Chai H, Yang Z, Liang Y, Chu G, Liu X. Application of 1/2 regularization logistic method in heart disease diagnosis. Biomedicalmaterials and Engineering 2014; 24(6): 3447-3454.10.3233/BME-14116925227056Search in Google Scholar

10. Buchan K, Filannino M, Uzuner Ö. Automatic prediction of coronary artery disease from clinical narratives, Journal of Biomedical Informatics, 2017; 72: 23-32.Search in Google Scholar

11. Anooj P. Clinical decision support system: risk level prediction of heart disease using weighted fuzzy rules and decision tree rules. Open Computer Science 2011; 1(4): 482-498.10.2478/s13537-011-0032-ySearch in Google Scholar

12. LeCun Y, Bengio Y, Hinton G. Deep Learning. Nature 2015; 521: 436-444.10.1038/nature1453926017442Search in Google Scholar

13. Xu J, Xiang L, Liu Q, Gilmore H, Wu J, Tang J, Madabhushi A. A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images. Neurocomputing 2016; 191: 214-223.10.1016/j.neucom.2015.11.133Open DOISearch in Google Scholar

14. Xu J, Xiang L, Liu Q, Gilmore H, Wu J, Tang J, Madabhushi A. Stacked sparse autoencoder (ssae) for nuclei detection on breast cancer histopathology images. IEEE Transactions on Medical Imaging 2016; 35(1): 119-130.10.1109/TMI.2015.2458702472970226208307Open DOISearch in Google Scholar

15. Badem H, Caliskan A, Basturk A, Yuksel ME. Classification and Diagnosis of the Parkinson Disease by Stacked Autoencoder. 10th International Conference on Electrical and Electronics Engineering ELECO 2016.Search in Google Scholar

16. Badem H, Caliskan A, Basturk A, Yuksel ME. Classification of Human Activity by Using a Stacked Autoencoder. Medical Technologies National Conference (TIPTEKNO’16) 2016.10.1109/TIPTEKNO.2016.7863135Search in Google Scholar

17. Badem H, Basturk A, Caliskan A, Yuksel ME. A new efficient training strategy for deep neural networks by hybridization of artificial bee colony and limited-memory BFGS optimization algorithms, Neurocomputing, Available online 1 June 201710.1016/j.neucom.2017.05.061Search in Google Scholar

18. Caliskan A, Yuksel ME, Badem H, Basturk A. A Deep Neural Network Classifier for Decoding Human Brain Activity Based on Magnetoencephalography. Elektronika Ir Elektrotechnika 2017; 23(2): 63-67.10.5755/j01.eie.23.2.18002Search in Google Scholar

19. Ghazi MM, Yanikoglu B, Aptoula E. Plant identification using deep neural networks via optimization of transfer learning parameters. Neurocomputing 2017.Search in Google Scholar

20. Lichman M. UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine CA: University of California.Search in Google Scholar

21. WHO url:http://www.who.int/mediacentre/factsheets/fs317/en/ available: 25.01.2017.Search in Google Scholar

22. Setiawan NA, Venkatachalam PA, Hani AFM. Diagnosis of coronary artery disease using artificial intelligence based decision support system. Proceedings of the international conference on man-machine systems (ICoMMS). Batu Ferringhi. Penang 2009.Search in Google Scholar

23. Anooj PK. Clinical decision support system: risk level prediction of heart disease using decision tree fuzzy rules. Int J Res Rev Comput Sci 2012; 3(3): 1659-1667.Search in Google Scholar

24. Rao A, Yadu N, Pimpalwar Y, Sinha S. Utility of coronary artery calcium scores in predicting coronary atherosclerosis amongst patients with moderate risk of coronary artery disease, Journal of Indian College of Cardiology 2017; 7: 55-59.Search in Google Scholar

25. Manabe O, Naya M, Tamaki N. Feasibility of PET for the management of coronary artery disease: Comparison between CFR and FFR, Journal of Cardiology 2017; 70(2): 135-14010.1016/j.jjcc.2017.03.00228318875Open DOISearch in Google Scholar

26. Ngiam J, Coates A, Lahiri A, Prochnow B, Le QV, Ng AY. On optimization methods for deep learning. Proceedings of the 28th International Conference on Machine Learning (ICML-11) 2011; 265-272.Search in Google Scholar

27. Bengio Y. Practical recommendations for gradient-based training of deep architectures. Neural Networks: Tricks of the Trade Springer 2012; 437- 478.10.1007/978-3-642-35289-8_26Open DOISearch in Google Scholar

28. Ng A. Sparse autoencoder. CS294A Lecture Notes. 2011.Search in Google Scholar

29. Zhang Y, Zhang E, Chen W. Deep neural network for halftone image classification based on sparse auto-encoder, Engineering Applications of Artificial Intelligence 2016; 50: 245-255.Search in Google Scholar

30. Évora LHRA., Seixas JM, Kritski AL. Neural network models for supporting drug and multidrug resistant tuberculosis screening diagnosis, Neurocomputing, Available online 6 June 2017.10.1016/j.neucom.2016.08.151Search in Google Scholar

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