Diagnostic Application of DNNS to Coronary Heart Disease


  • Shaik Ghouhar Taj, Kalaivani Kathirvelu


Accuracy, cardiovascular disease, classification, coronary artery disease, diagnosis, heart disease.


According to the World Health Organization (WHO), CVD is the leading cause of mortality globally. In 2015, CVD was responsible for more than 75% deaths that occurred in worldwide. In US, due to heart disease approximately 630,000 fatalities annually, accounting for 25% of all deaths. In 2015, coronary heart disease was the top cause of mortality in the US, claiming the lives of over 360,000 Americans. This information underscores the importance of addressing cardiovascular disease as a major global health concern and highlights the potential of advanced technology, such as deep neural network learning, to aid in the early detection and prediction of coronary heart disease, ultimately benefiting healthcare on a global scale. The DNN model utilized regularization, dropout techniques, and an improved multilayer perception architecture. DNN learning model achieved impressive performance metrics viz. F-score: 0.9571, area under the ROC curve: 0.9812, Kolmogorov-Smirnov (K-S) test: 67.62, diagnostic odds ratio: 39.75, 95% confidence interval: [38.65, 110.28], accuracy: 84.67%, sensitivity: 94.51%, specificity: 73.86%, precision: 80.12%. A dataset containing 303 clinical occurrences was used for training the model. The application of such models can contribute to improving public health and global health outcomes. These models have the potential to assist healthcare professionals and patients worldwide, especially in low-income and resource-constrained settings where cardiac experts are scarce.


Download data is not yet available.


World Health Organization (WHO), "Cardiovascular diseases (CVD)," http://www.who.int/news-room/fact-sheets/detail/cardiovasculardiseases-(cvds), 2017.

Centers for Disease Control and Prevention (CDC), "Heart disease fact sheet, "https://www.cdc.gov/dhdsp/data_statistics/fact_sheets/fs_heart_di sease.htm, 2015.

E. J. Benjamin, S. S. Virani, C. W. Callaway, A. R. Chang, S. Cheng, S. E. Chiuve, M. Cushman, and F. N. Delling, et. al., "Heart disease and stroke statistics-2018 update: a report from the American Heart Association," Circulation, Vol. 137, No. 12, pp. 67- 492, 2018.

Centers for Disease Control and Prevention, "Facts about heart disease,"

http://www.cdc.gov/heartdisease/docs/consumered_heartdisease.pdf, 2013.

E. G. Nabel and E. Braunwald, “A tale of coronary artery disease and myocardial infarction," New England Journal of Medicine, Vol. 366, pp. 54-63, 2012.

M. Shouman, T. Turner, and R. Stocker, “Using decision tree for diagnosing heart disease patients,” Proceedings of the 9th Australasian Data Mining Conference, pp. 23–29, Ballarat, Australia, 2011.

O. Y. Atkov, S. G. Gorokhova, A. G. Sboev, E. V. Generozov, E. V. Muraseyeva, S. Y. Moroshkina, and N. N.Cherniy, "Coronary heart disease diagnosis by artificial neural networks including genetic polymorphisms and clinical parameters," Journal of Cardiology, Vol. 59, Issue 2, pp. 190-194, 2012.

D. Dolatabadi, S. E. Khadem, B. M. Asl, “Automated diagnosis of coronary artery disease (CAD) patients using optimized SVM,” Computer Methods and Programs in Biomedicine, Vol. 138, Issue C, pp. 117-126, 2017.

P. Gayathri and N. Jaisankar, “Comprehensive study of heart disease diagnosis using data mining and soft computing techniques,” International Journal of Engineering and Technology, Vol. 5, No. 3, pp. 2947-2957, 2013.

K. H. Miao, J. H. Miao, and G. J. Miao, "Diagnosing heart disease using ensemble machine learning," International Journal of Advanced Computer Science and Applications, Vol. 7, No. 10, pp. 30-39, 2016.

H. M. Sawbwa, N. Ghali, and A. E. Hassanien, "Detection of heart disease using binary particle swarm optimization," Proceedings of the Federated Conference on Computer Science and Information Systems, pp. 177-182, 2012.

Ozcift and A. Gulten, “Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms,” Journal of Computer Methods and Programs in Biomedicine, Vol. 104, pp. 443-451, 2011.

L. Pasi and L. Jouni, “A classification method based on principal component analysis and differential evolution algorithm applied for prediction diagnosis from clinical EMR heart data sets,” Computational Intelligence in Optimization, Vol. 7, pp. 263-283, 2010.

W. Wiharto, H. Kusnanto, and H. Herianto, “Intelligence system for diagnosis level of coronary heart disease with K-star algorithm,” Healthcare Informatics Research, Vol. 22, No. 1, pp. 30-38, 2016.

R. Detrano, A. Janosi, A. Steinbrunn, W. Pfisterer, M. Schmid, J. Sandhu, S. Guppy, K. Lee, and V. Froelicher, “International application of a new probability algorithm for the diagnosis of coronary artery disease,” American Journal of Cardiology, Vol. 64, pp. 304-310, 1989.

D. Pal, K. Mandana, S. Pal, D. Sarkar, and C. Chakraborty, “Fuzzy expert system approach for coronary artery disease screening using clinical parameters,” Knowledge-Based System, Vol. 36, pp. 162-174, 2012.

H. R. Marateb and S. Goudarzi, “A noninvasive method for coronary artery diseases diagnosis using a clinically-interpretable fuzzy rule based system,” Journal of Research in Medical Sciences, Vol. 20, No. 3, pp. 214-223, 2015.

J. Schmidhuber, "Deep learning in neural networks: An Overview." Neural Networks, Vol. 61, pp. 85-117, 2015.

J. H. Miao and K. H. Miao, "Cardiotocographic diagnosis of fetal health based on multiclass morphologic pattern predictions using deep learning classification," International Journal of Advanced Computer Science and Applications, Vol. 9, No. 5, pp. 1-11, 2018.

D. J. Newman, S. Hettich, C. L. Blake, and C. J. Merz, UCI Repository of Machine Learning Databases, University California Irvine, Department of Information and Computer Science, 2018.

Y. Bengio, "Learning deep architectures for AI", Foundations and Trends in Machine Learning, Vol. 2, No. 1, pp 1-127, 2009.

Goodfellow, Y. Bengio, and A. Courville, Deep Learning, The MIT Press, 2016.

F. Q. Lauzon, "An introduction to deep learning," The 11th International Conference on Information Science, Signal Processing and their Applications, Montreal, QC, pp. 1438-1439, 2012.

G. J. Miao, K. H. Miao, and J. H. Miao, “Neural pattern recognition model for breast cancer diagnosis,” Journal of Selected Areas in Bioinformatics, Vol. 2, Issue 8, pp. 1–8, 2012.

C. Gavin, and T. L. C. Nicola, “On over-fitting in model selection and subsequent selection bias in performance evaluation,” Journal of Machine Learning Research, Vol. 11, pp. 2079−2107, 2010.

Kukačka, V. Golkov, and D. Cremers, “Regularization for deep learning: a taxonomy,” Artificial Intelligence, pp 1-27, 2017.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” Journal of Machine Learning Research, Vol. 15, pp. 1929- 1958, 2014.

G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov, “Improving neural networks by preventing co-adaptation of feature detectors,” Neural and Evolutionary Computing, pp. 1-18, 2012.

D. Warde-Farley, I. J. Goodfellow, A. Courville, and Y. Bengio, “An empirical analysis of dropout in piecewise linear networks,” Machine Learning, pp. 1-10, 2014.

H. Miao and G. J. Miao, “Mammographic diagnosis for breast cancer biopsy predictions using neural network classification model and receiver operating characteristic (ROC) curve evaluation,” Multidisciplinary Journals in Science and Technology, Journal of Selected Areas in Bioinformatics, Vol. 3, Issue 9, pp. 1–10, 2013.

J. H. Miao, K. H. Miao, and G. J. Miao, “Breast cancer biopsy predictions based on mammographic diagnosis using Support Vector Machine learning,” Multidisciplinary Journals in Science and Technology, Journal of Selected Areas in Bioinformatics, Vol. 5, No. 4, pp. 1–9, 2015.

Sokolova, N. Japkowicz, and S. Szpakowicz, “Beyond accuracy, Fscore and ROC: a family of discriminant measures for performance evaluation.,” A. Sattar and B. Kang (eds), Advances in Artificial Intelligence, Lecture Notes in Computer Science, Vol. 4304, Berlin, Heidelberg, 2006.

A. M. Law, “A tutorial on how to select simulation input probability distributions,” The IEEE Proceedings of the 2013 Winter Simulation Conference, pp. 306–320, 2013.

T. B. Arnold and J. W. Emerson, “Nonparametric goodness-of-fit tests for discrete null distributions,” The R Journal, Vol. 3/2, pp. 34-39, 2011.

A. S. Glas, J. G. Lijmer, M. H. Prins, G. J. Bonsel, and P. M. M. Bossuyt, "The diagnostic odds ratio: a single indicator of test performance". Journal of Clinical Epidemiology, Vol. 56, No. 11, pp. 1129–1135, 2003.

G. J. Miao and M. A. Clements, Digital Signal Processing and Statistical Classification, Artech House, Inc., 2002.

C. Tu and D. Shin, “Effective diagnosis of heart disease through Bagging approach,” The IEEE 2nd International Conference on Biomedical Engineering and Informatics, pp. 1-4, 2009.

G. Hinton, “A practical guide to training Restricted Boltzmann Machines,” University of Toronto, Department of Computer Science, Version 1, pp. 1-21, 2010.

Q. V. Le, “A tutorial on deep learning part 2: autoencoders, convolutional neural networks and recurrent neural networks,” Google Inc, Mountain View, California, pp. 1-20, 2015.

Y. LeCun, Y. Bengio, and G. Hinton. "Deep Learning" Nature, Vol. 521, pp 436-444, 2015.




How to Cite

Shaik Ghouhar Taj. (2024). Diagnostic Application of DNNS to Coronary Heart Disease. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 2660–2670. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5868



Research Article