Research Article
Year 2020,
Volume: 42 Issue: 3, 283 - 289, 27.10.2020
Abstract
References
- 1. Box LC, Angiolillo DJ, Suzuki N, Box LA, Jian J, Guzman L, et al. Heterogeneity of atherosclerotic plaque characteristics in human coronary artery disease: A three-dimensional intravascular ultrasound study. Catheter Cardiovasc Interv 2007;70(3):349-56. https://doi.org/10.1002/ccd.21088.
- 2. Alonso DH, Wernick MN, Yang Y, Germano G, Berman DS, Slmoka P. Prediction of cardiac death after adenosine myocardial perfusion SPECT based on machine learning. J Nucl Cardiol 2018. https://doi.org/10.1007/s12350-017-0924-x.
- 3. Narula S, Shameer K, Salem Omar AM, Dudley JT, Sengupta PP. Reply: Deep learning with unsupervised feature in echocardiographic imaging. J Am Coll Cardiol 2017;69:2101–2.
- 4. Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform 2017 May 6.
- 5. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542:115–8.
- 6. Shameer K, Johnson KW, Glicksberg BS, Dudley JT, Sengupta PP. Machine learning in cardiovascular medicine: Are we there yet? Heart 2018. https://doi.org/10.1136/heartjnl-2017-311198.
- 7. Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS One 2017;12:e0174944.
- 9. C. E. Rasmussen and C. K. I. Williams, Gaussian processes for machine learning. Cambridge, Mass.: MIT Press, 2006.
- 10. M. Stone, "Cross-Validatory Choice and Assessment of Statistical Predictions," Journal of the Royal Statistical Society. Series B (Methodological), vol. 36, pp. 111-147, 1974.
- 11. T. Cover and P. Hart, "Nearest neighbor pattern classification,"Information Theory, IEEE Transactions on, vol. 13, pp. 21-27, 1967.
- 12. Savarese G, Lund LH. Global public health burden of heart failure. Cardiac Fail Rev. 2017;3(1):7–11. https://doi.org/10.15420/cfr.2016:25:2.
- 13. Braunschweig F, Cowie MR, Auricchio A. What are the costs of heart failure? Europace. 2011;13(Suppl 2):ii13–7. https://doi.org/10.1093/europace/eur081.
- 14. Sanders-van Wijk S, van Asselt AD, Rickli H, Estlinbaum W, Erne P, Rickenbacher P, et al. Cost-effectiveness of N-terminal pro-B-type natriuretic-guided therapy in elderly heart failure patients: results from TIME-CHF (Trial of Intensified versus Standard Medical Therapy in Elderly Patients with Congestive Heart Failure). JACC Heart Fail. 2013;1(1):64–71. https://doi.org/10.1016/j.jchf.2012.08.002.
- 15. Conrad N, Judge A, Tran J, Mohseni H, Hedgecott D, Crespillo AP, et al. Temporal trends and patterns in heart failure incidence: a population-based study of 4 million individuals. Lancet. 2018;391(10120):572–80. https://doi.org/10.1016/s0140-6736(17)32520-5.
- 16. KrumH, Forbes A, Yallop J, Driscoll A, Croucher J, Chan B, et al. Telephone support to rural and remote patients with heart failure: the Chronic Heart Failure Assessment by Telephone (CHAT) study. Cardiovasc Ther. 2013;31(4):230–7. https://doi.org/10.1111/17555922.12009.
- 17. Tran BX,. Latkin C A , Giang VT, et al. The Current Research Landscape of the Application of Artificial Intelligence in Managing Cerebrovascula and Heart Diseases: A Bibliometric and Content Analysis. Int. J. Environ. Res. Public Health 2019;16:2699. https://doi:10.3390/ijerph16152699.
- 18. Pakhomov, S.S.; Hemingway, H.;Weston, S.A.; Jacobsen, S.J.; Rodehe er, R.; Roger, V.L. Epidemiology of angina pectoris: Role of natural language processing of the medical record. Am. Heart J. 2007, 153, 666–673.
- 19. Kwon, J.M.; Kim, K.H.; Jeon, K.H.; Park, J. Deep learning for predicting in-hospital mortality among heart disease patients based on echocardiography. Echocardiography 2019, 36, 213–218.
- 20. Herweh, C.A, Ringleb, P, Rauch, G, Gerry, S, Behrens, L, Möhlenbruch, M, Gottorf, et al. Performance of e-ASPECTS software in comparison to that of stroke physicians on assessing CT scans of acute ischemic stroke patients. Int. J. Stroke 2016, 11, 438–445.
- 21. Char, D.S.; Shah, N.H.; Magnus, D. Implementing Machine Learning in Health Care—Addressing Ethical Challenges. N. Engl. J. Med. 2018, 378, 981–983.
- 22. Beck, A.H.; Sangoi, A.R.; Leung, S.; Marinelli, R.J.; Nielsen, T.O.; Van De Vijver, M.J.; West, R.B.; Van De Rijn, M.; Koller, D. Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival. Sci. Transl. Med. 2011, 3, 108–113.
- 23. Hae, H.; Kang, S.J.; Kim,W.J.; Choi, S.Y.; Lee, J.G.; Bae, Y.; Cho, H.; Yang, D.H.; Kang, J.W.; Lim, T.H.; et al. Machine learning assessment of myocardial ischemia using angiography: Development and retrospective validation. PLoS Med. 2018, 15, e1002693.
Year 2020,
Volume: 42 Issue: 3, 283 - 289, 27.10.2020
Abstract
Objective: The aim of this study is to determine whether people have heart disease by using different machine learning algorithms with the data provided by the University of Cleveland.
Method: 303 patient data provided by the University of Cleveland were classified using Gaussian Bayes, K-Nearest Neighbor and Random Forest Algorithms with and without feature scaling. With each algorithm, the data is divided into random training and test sets. This process was repeated 50 times for each algorithm. The test results were subjected to the T-test to check statistical independence.
Results: In this study, 80.52% accuracy with K-Nearest Neighbor algorithm, 80.52% with Gaussian Bayes and 82.50% with Random Forest were observed with data scaling. The results of the three algorithms produced similar values and did not show statistical independence (p> 0.05). Without data scaling, 65.28% accuracy with the K-Nearest Neighbor algorithm, 80.52% with Gaussian Bayes and 82.19% with Random Forest were observed. The test results obtained with three algorithms showed statistical independence.
Conclusions: Although there were data from 303 patients in the study, over 80% accurate prediction was obtained. The presence of endpoints that distort the distribution in the data used results in differences in the methods used. It has been confirmed that much closer estimates can be obtained on a scaled patient data. This study is an example of the use of artificial intelligence in detecting cardiac diseases that pose a risk all over the world. With a more detailed patient data, much higher accuracy rates can be obtained and included in health management processes in the pre-diagnosis of heart disease in the future.
References
- 1. Box LC, Angiolillo DJ, Suzuki N, Box LA, Jian J, Guzman L, et al. Heterogeneity of atherosclerotic plaque characteristics in human coronary artery disease: A three-dimensional intravascular ultrasound study. Catheter Cardiovasc Interv 2007;70(3):349-56. https://doi.org/10.1002/ccd.21088.
- 2. Alonso DH, Wernick MN, Yang Y, Germano G, Berman DS, Slmoka P. Prediction of cardiac death after adenosine myocardial perfusion SPECT based on machine learning. J Nucl Cardiol 2018. https://doi.org/10.1007/s12350-017-0924-x.
- 3. Narula S, Shameer K, Salem Omar AM, Dudley JT, Sengupta PP. Reply: Deep learning with unsupervised feature in echocardiographic imaging. J Am Coll Cardiol 2017;69:2101–2.
- 4. Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform 2017 May 6.
- 5. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542:115–8.
- 6. Shameer K, Johnson KW, Glicksberg BS, Dudley JT, Sengupta PP. Machine learning in cardiovascular medicine: Are we there yet? Heart 2018. https://doi.org/10.1136/heartjnl-2017-311198.
- 7. Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS One 2017;12:e0174944.
- 9. C. E. Rasmussen and C. K. I. Williams, Gaussian processes for machine learning. Cambridge, Mass.: MIT Press, 2006.
- 10. M. Stone, "Cross-Validatory Choice and Assessment of Statistical Predictions," Journal of the Royal Statistical Society. Series B (Methodological), vol. 36, pp. 111-147, 1974.
- 11. T. Cover and P. Hart, "Nearest neighbor pattern classification,"Information Theory, IEEE Transactions on, vol. 13, pp. 21-27, 1967.
- 12. Savarese G, Lund LH. Global public health burden of heart failure. Cardiac Fail Rev. 2017;3(1):7–11. https://doi.org/10.15420/cfr.2016:25:2.
- 13. Braunschweig F, Cowie MR, Auricchio A. What are the costs of heart failure? Europace. 2011;13(Suppl 2):ii13–7. https://doi.org/10.1093/europace/eur081.
- 14. Sanders-van Wijk S, van Asselt AD, Rickli H, Estlinbaum W, Erne P, Rickenbacher P, et al. Cost-effectiveness of N-terminal pro-B-type natriuretic-guided therapy in elderly heart failure patients: results from TIME-CHF (Trial of Intensified versus Standard Medical Therapy in Elderly Patients with Congestive Heart Failure). JACC Heart Fail. 2013;1(1):64–71. https://doi.org/10.1016/j.jchf.2012.08.002.
- 15. Conrad N, Judge A, Tran J, Mohseni H, Hedgecott D, Crespillo AP, et al. Temporal trends and patterns in heart failure incidence: a population-based study of 4 million individuals. Lancet. 2018;391(10120):572–80. https://doi.org/10.1016/s0140-6736(17)32520-5.
- 16. KrumH, Forbes A, Yallop J, Driscoll A, Croucher J, Chan B, et al. Telephone support to rural and remote patients with heart failure: the Chronic Heart Failure Assessment by Telephone (CHAT) study. Cardiovasc Ther. 2013;31(4):230–7. https://doi.org/10.1111/17555922.12009.
- 17. Tran BX,. Latkin C A , Giang VT, et al. The Current Research Landscape of the Application of Artificial Intelligence in Managing Cerebrovascula and Heart Diseases: A Bibliometric and Content Analysis. Int. J. Environ. Res. Public Health 2019;16:2699. https://doi:10.3390/ijerph16152699.
- 18. Pakhomov, S.S.; Hemingway, H.;Weston, S.A.; Jacobsen, S.J.; Rodehe er, R.; Roger, V.L. Epidemiology of angina pectoris: Role of natural language processing of the medical record. Am. Heart J. 2007, 153, 666–673.
- 19. Kwon, J.M.; Kim, K.H.; Jeon, K.H.; Park, J. Deep learning for predicting in-hospital mortality among heart disease patients based on echocardiography. Echocardiography 2019, 36, 213–218.
- 20. Herweh, C.A, Ringleb, P, Rauch, G, Gerry, S, Behrens, L, Möhlenbruch, M, Gottorf, et al. Performance of e-ASPECTS software in comparison to that of stroke physicians on assessing CT scans of acute ischemic stroke patients. Int. J. Stroke 2016, 11, 438–445.
- 21. Char, D.S.; Shah, N.H.; Magnus, D. Implementing Machine Learning in Health Care—Addressing Ethical Challenges. N. Engl. J. Med. 2018, 378, 981–983.
- 22. Beck, A.H.; Sangoi, A.R.; Leung, S.; Marinelli, R.J.; Nielsen, T.O.; Van De Vijver, M.J.; West, R.B.; Van De Rijn, M.; Koller, D. Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival. Sci. Transl. Med. 2011, 3, 108–113.
- 23. Hae, H.; Kang, S.J.; Kim,W.J.; Choi, S.Y.; Lee, J.G.; Bae, Y.; Cho, H.; Yang, D.H.; Kang, J.W.; Lim, T.H.; et al. Machine learning assessment of myocardial ischemia using angiography: Development and retrospective validation. PLoS Med. 2018, 15, e1002693.
There are 22 citations in total.
Details
| Primary Language | English |
|---|---|
| Subjects | Health Care Administration |
| Journal Section | Medical Science Research Articles |
| Authors | |
| Publication Date | October 27, 2020 |
| Acceptance Date | August 17, 2020 |
| Published in Issue | Year 2020Volume: 42 Issue: 3 |