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.
2. Bounhas M, Mellouli K, Prade H, Serrurier M. Possibilistic classifiers for numerical data. Soft Computing 2012; 17(5): 733-751.
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.
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.
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.
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.
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.
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.
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. Buchan K, Filannino M, Uzuner Ö. Automatic prediction of coronary artery disease from clinical narratives, Journal of Biomedical Informatics, 2017; 72: 23-32.
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.
12. LeCun Y, Bengio Y, Hinton G. Deep Learning. Nature 2015; 521: 436-444.
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.
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.
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.
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.
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 2017
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.
19. Ghazi MM, Yanikoglu B, Aptoula E. Plant identification using deep neural networks via optimization of transfer learning parameters. Neurocomputing 2017.
20. Lichman M. UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine CA: University of California.
21. WHO url:http://www.who.int/mediacentre/factsheets/fs317/en/ available: 25.01.2017.
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.
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.
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.
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): 135-140
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.
27. Bengio Y. Practical recommendations for gradient-based training of deep architectures. Neural Networks: Tricks of the Trade Springer 2012; 437- 478.
28. Ng A. Sparse autoencoder. CS294A Lecture Notes. 2011.
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.
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.