Coronary Heart Disease Interpretation Based on Deep Neural Network

Annisa Darmawahyuni; Siti Nurmaini; Firdaus Firdaus

doi:10.18495/comengapp.v8i1.288

[1] W. H. Organization and others, “World health statistics 2017: monitoring health for the SDGs, sustainable development goals,” 2017.
[2] R. I. Kementrian Kesehatan, “Riset kesehatan dasar,” Jakarta Kementerian. Kesehatan. RI, 2013.
[3] NHLBI, “Coronary Heart Disease.” [Online]. Available: https://www.nhlbi.nih.gov/health-topics/coronary-heart-disease.
[4] D. R. Chowdhury, M. Chatterjee, and R. K. Samanta, “An artificial neural network model for neonatal disease diagnosis,” Int. J. Artif. Intell. Expert Syst., vol. 2, no. 3, pp. 96–106, 2011.
[5] G. Subbalakshmi, K. Ramesh, and M. C. Rao, “Decision support in heart disease prediction system using naive bayes,” Indian J. Comput. Sci. Eng., vol. 2, no. 2, pp. 170–176, 2011.
[6] J. Nahar, T. Imam, K. S. Tickle, and Y.-P. P. Chen, “Computational intelligence for heart disease diagnosis: A medical knowledge driven approach,” Expert Syst. Appl., vol. 40, no. 1, pp. 96–104, 2013.
[7] E. O. Olaniyi, O. K. Oyedotun, A. Helwan, and K. Adnan, “Neural network diagnosis of heart disease,” in Advances in Biomedical Engineering (ICABME), 2015 International Conference on, 2015, pp. 21–24.
[8] Z. Xing, J. Pei, and E. Keogh, “A brief survey on sequence classification,” ACM Sigkdd Explor. Newsl., vol. 12, no. 1, pp. 40–48, 2010.
[9] Y. Zheng, Q. Liu, E. Chen, Y. Ge, and J. L. Zhao, “Time series classification using multi-channels deep convolutional neural networks,” in International Conference on Web-Age Information Management, 2014, pp. 298–310.
[10] N. A. Sundar, P. P. Latha, and M. R. Chandra, “Performance analysis of classification data mining techniques over heart disease database,” IJESAT] Int. J. Eng. Sci. Adv. Technol. ISSN, pp. 2250–3676, 2012.
[11] V. Chaurasia and S. Pal, “Data mining approach to detect heart diseases,” 2014.
[12] A. A. Shinde, “Heart Disease Prediction System using Multilayered Feed Forward Neural Network and Back Propagation Neural Network,” 2017.
[13] A. Janosi, “UCI Machine Learning Repository Heart Disease Dataset.” [Online]. Available: http://archive.ics.uci.edu/ml/datasets/Heart+Disease. [Accessed: 06-Dec-2018].
[14] Y. K. Jain and S. K. Bhandare, “Min max normalization based data perturbation method for privacy protection,” Int. J. Comput. Commun. Technol., vol. 2, no. 8, pp. 45–50, 2011.
[15] F. Amato, A. López, E. M. Peña-Méndez, P. Va\vnhara, A. Hampl, and J. Havel, “Artificial neural networks in medical diagnosis.” Elsevier, 2013.
[16] N. Larasati, T. Dewi, and Y. Oktarina, “Object following design for a mobile robot using neural network,” Comput. Eng. Appl. J., vol. 6, no. 1, pp. 5–14, 2017.
[17] Y. Lei, N. Scheffer, L. Ferrer, and M. McLaren, “A novel scheme for speaker recognition using a phonetically-aware deep neural network,” in Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on, 2014, pp. 1695–1699.
[18] S. Nurmaini, A. Gani, and others, “Cardiac Arrhythmias Classification Using Deep Neural Networks and Principle Component Analysis Algorithm.,” Int. J. Adv. Soft Comput. Its Appl., vol. 10, no. 2, 2018.
[19] P.-T. De Boer, D. P. Kroese, S. Mannor, and R. Y. Rubinstein, “A tutorial on the cross-entropy method,” Ann. Oper. Res., vol. 134, no. 1, pp. 19–67, 2005.
[20] S. Ronaghan, “Deep Learning: Which Loss and Activation Functions should I use?” [Online]. Available: https://towardsdatascience.com/deep-learning-which-loss-and-activation-functions-should-i-use-ac02f1c56aa8.
[21] X. Deng, Q. Liu, Y. Deng, and S. Mahadevan, “An improved method to construct basic probability assignment based on the confusion matrix for classification problem,” Inf. Sci. (Ny)., vol. 340, pp. 250–261, 2016.
[22] S. Pintea and R. Moldovan, “The receiver-operating characteristic (ROC) analysis: Fundamentals and applications in clinical psychology.,” J. Cogn. Behav. Psychother., vol. 9, no. 1, 2009.
[23] T. Fawcett, “An introduction to ROC analysis,” Pattern Recognit. Lett., vol. 27, no. 8, pp. 861–874, 2006.
[24] J.-S. Wang, W.-C. Chiang, Y.-L. Hsu, and Y.-T. C. Yang, “ECG arrhythmia classification using a probabilistic neural network with a feature reduction method,” Neurocomputing, vol. 116, pp. 38–45, 2013.
[25] R. Kumar and A. Indrayan, “Receiver operating characteristic (ROC) curve for medical researchers,” Indian Pediatr., vol. 48, no. 4, pp. 277–287, 2011.
[26] K. H. Zou, “Receiver operating characteristic (ROC) literature research,” On-line Bibliogr. available from< http//splweb. bwh. harvard. edu, vol. 8000, 2002.
[27] G. E. Dahl, T. N. Sainath, and G. E. Hinton, “Improving deep neural networks for LVCSR using rectified linear units and dropout,” in Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, 2013, pp. 8609–8613.

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Affiliations

Annisa Darmawahyuni
Universitas Sriwijaya

Siti Nurmaini
Universitas Sriwijaya

Firdaus Firdaus
Universitas Sriwijaya

Content Top

Coronary Heart Disease Interpretation Based on Deep Neural Network

Annisa Darmawahyuni ,
Siti Nurmaini ,
Firdaus Firdaus

Vol 8 No 1 (2019)

DOI 10.18495/comengapp.v8i1.288

Submitted: Dec 26, 2018

Published: Feb 1, 2019

Abstract

Coronary heart disease (CHD) population increases every year with a significant number of deaths. Moreover, the mortality from coronary heart disease gets the highest prevalence in Indonesia at 1.5 percent. The misdiagnosis of coronary heart disease is a crucial fundamental that is the major factor that caused death. To prevent misdiagnosis of CHD, an intelligent system has been designed. This paper proposed a simulation which can be used to diagnose the coronary heart disease in better performance than the traditional diagnostic methods. Some researches have developed a system using conventional neural network or other machine learning algorithm, but the results are not a good performance. Based on a conventional neural network, deeper neural network (DNN) is proposed to our model in this work. As known as, the neural network is a supervised learning algorithm that good in the classification task. In DNN model, the implementation of binary classification was implemented to diagnose CHD present (representative “1”) or CHD absent (representative “0”). To help performance analysis using the UCI machine learning repository heart disease dataset, ROC Curve and its confusion matrix were implemented in this work. The overall predictive accuracy, sensitivity, and specificity acquired was 96%, 99%, 92%, respectively.