[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.2012.07.032]Search 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/s00500-012-0947-9]Open 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.2013.6920488]Search 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.2014.7008032]Search 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.2014.11.091]Search 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.2014.7058126]Search 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.2013.6717845]Search 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/s13369-013-0934-1]Search 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-14116925227056]Search 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-y]Search in Google Scholar
[12. LeCun Y, Bengio Y, Hinton G. Deep Learning. Nature 2015; 521: 436-444.10.1038/nature1453926017442]Search 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.2015.11.133]Open 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.2015.2458702472970226208307]Open 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.2016.7863135]Search 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.2017.05.061]Search 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.23.2.18002]Search 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.2017.03.00228318875]Open 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_26]Open 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.2016.08.151]Search in Google Scholar