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Open Access

A Clinical Data Analysis Based Diagnostic Systems for Heart Disease Prediction Using Ensemble Method

Department of Computer Engineering and Applications, GLA University, Mathura 281406, India
School of Computing, Graphic Era Hill University, Dehradun 248002, India
Department of Electronics and Communication Engineering, GLA University, Mathura 281406, India
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Abstract

The correct diagnosis of heart disease can save lives, while the incorrect diagnosis can be lethal. The UCI machine learning heart disease dataset compares the results and analyses of various machine learning approaches, including deep learning. We used a dataset with 13 primary characteristics to carry out the research. Support vector machine and logistic regression algorithms are used to process the datasets, and the latter displays the highest accuracy in predicting coronary disease. Python programming is used to process the datasets. Multiple research initiatives have used machine learning to speed up the healthcare sector. We also used conventional machine learning approaches in our investigation to uncover the links between the numerous features available in the dataset and then used them effectively in anticipation of heart infection risks. Using the accuracy and confusion matrix has resulted in some favorable outcomes. To get the best results, the dataset contains certain unnecessary features that are dealt with using isolation logistic regression and Support Vector Machine (SVM) classification.

Keywords

artificial intelligencesupport vector machinelogistic regressioncleveland datasetsupervised algorithmhuman sensing

References

[1]
F. Ali, S. El-Sappagh, S. M. R. Islam, D. Kwak, A. Ali, M. Imran, and K. S. Kwak, A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion, Information Fusion, vol. 63, pp. 208–222, 2020.
Crossref Google Scholar
[2]
M. M. Ali, B. K. Paul, K. Ahmed, F. M. Bui, J. M. W. Quinn, and M. A. Moni, Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison, Computers in Biology and Medicine, vol. 136, p. 104672, 2021.
Crossref Google Scholar
[3]
M. S. Amin, Y. K. Chiam, and K. D. Varathan, Identification of significant features and data mining techniques in predicting heart disease, Telematics and Informatics, vol. 36, pp. 82–93, 2019.
Crossref Google Scholar
[4]
K. Andjelkovic, D. K. Ostric, and I. Andjelkovic, Prediction of heart failure in adults with congenital heart disease, European Journal of Heart Failure, vol. 16, p. 87, 2014.
Google Scholar
[5]
T. H. S. Dent, Predicting the risk of coronary heart disease. II: The role of novel molecular biomarkers and genetics in estimating risk, and the future of risk prediction, Atherosclerosis, vol. 213, no. 2, pp. 352–362, 2010.
Crossref Google Scholar
[6]
K. Dimopoulos, K. Muthiah, R. Alonso-Gonzalez, S. J. Wort, G. P. Diller, M. A. Gatzoulis, and A. Kempny, Heart or heart and lung transplantation for patients with congenital heart disease in England: Outcomes and future predictions, Circulation, vol. 134, p. A17841, 2016.
Google Scholar
[7]
A. S. Dontas, A. Menotti, C. Aravanis, A. Corcondilas, D. Lekos, and F. Seccareccia, Long-term prediction of coronary heart disease mortality in two rural Greek populations, European Heart Journal, vol. 14, no. 9, pp. 1153–1157, 1993.
Crossref Google Scholar
[8]
M. G. Elshafie, A. Hagag, E. S. A. El-Dahshan, and M. A. Ismail, A hybrid bidirectional LSTM and 1D CNN for heart disease prediction, International Journal of Computer Science and Network Security, vol. 21, no. 10, pp. 135–144, 2021.
Google Scholar
[9]
O. E. Emam, A. Abdo, and M. M. Mahmoud, An adaptive heart disease behavior-based prediction system, International Journal of Advanced Computer Science and Applications, vol. 10, no. 1, pp. 374–383, 2019.
Crossref Google Scholar
[10]
L. Harel-Sterling, F. Wang, S. Cohen, A. Liu, J. Brophy, G. Paradis, and A. Marelli, Risk predictions in adult congenital heart disease patients with heart failure: A systematic review, Journal of the American College of Cardiology, vol. 73, no. 9, p. 656, 2019.
Crossref Google Scholar
[11]
Y. L. Huang and A. Sajid, Prediction model of pathogenic gene of coronary heart disease based on machine learning, Basic & Clinical Pharmacology & Toxicology, vol. 122, p. 32, 2018.
Google Scholar
[12]
R. Katarya and S. K. Meena, Machine learning techniques for heart disease prediction: A comparative study and analysis, Health and Technology, vol. 11, no. 1, pp. 87–97, 2021.
Crossref Google Scholar
[13]
M. Kavousi, S. Elias-Smale, J. H. Rutten, R. V. Proenca, M. Oudkerk, M. P. D. Maat, A. Hofman, E. Steyerberg, A. H. V. D. Meiracker, and J. C. Witteman, Comparison of novel markers in prediction of coronary heart disease risk: The rotterdam study, Circulation, vol. 122, no. 21, p. A17770, 2010.
Google Scholar
[14]
Erica Research Group, Prediction of coronary heart-disease in Europe. The 2nd report of the WHO-ERICA project, European Heart Journal, vol. 12, no. 3, pp. 291–297, 1991.
Crossref Google Scholar
[15]
C. Mufudza and H. Erol, Poisson mixture regression models for heart disease prediction, Computational and Mathematical Methods in Medicine, vol. 2016, p. 4083089, 2016.
Crossref Google Scholar
[16]
K. Saaksjarvi, P. Knekt, and S. Mannisto, The prediction of a diet quality index on cardiovascular disease and coronary heart disease mortality, Annals of Nutrition and Metabolism, vol. 67, p. 327, 2015.
Google Scholar
[17]
S. C. Siu, J. Grewal, M. Sermer, J. Mason, M. Kiess, R. Wald, J. Colman, and C. Silversides, Comprehensive prediction of cardiac outcomes in pregnant women with heart disease, Circulation, vol. 136, p. A15606, 2017.
Google Scholar
[18]
C. Sowmiya and P. Sumitra, A hybrid approach for mortality prediction for heart patients using ACO-HKNN, Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 5, pp. 5405–5412, 2021.
Crossref Google Scholar
[19]
H. T. Cate, F. Kontny, and D. W. Nilsen, Editorial: Novel and potential markers for prediction of outcome in patients with acute and chronic coronary heart disease, Frontiers in Cardiovascular Medicine, vol. 6, p. 66, 2019.
Crossref Google Scholar
[20]
M. Tomasdottir, N. Hadziosmanovic, C. Held, P. E. Aylward, A. Budaj, C. P. Cannon, J. Engdahl, C. B. Granger, W. Koenig, A. J. Manolis, et al. , Biomarkers improve the prediction of incident atrial fibrillation in patients with stable coronary heart disease, Circulation, vol. 138, p. A15437, 2018.
Google Scholar
[21]
S. Udhan and B. Patil, A systematic review of Machine learning techniques for heart disease prediction, International Journal of Next-Generation Computing, vol. 12, no. 2, pp. 229–239, 2021.
Google Scholar
[22]
X. M. Yuan, J. H. Chen, K. Zhang, Y. Wu, and T. T. Yang, A stable AI-based binary and multiple class heart disease prediction model for IoMT, IEEE Transactions on Industrial Informatics, vol. 18, no. 3, pp. 2032–2040, 2022.
Crossref Google Scholar
[23]
D. Q. Zhang, Y. Y. Chen, Y. X. Chen, S. Y. Ye, W. Y. Cai, J. X. Jiang, Y. C. Xu, G. F. Zheng, and M. Chen, Heart disease prediction based on the embedded feature selection method and deep neural network, Journal of Healthcare Engineering, vol. 2021, p. 6260022, 2021.
Crossref Google Scholar
Big Data Mining and Analytics
Volume 6 Issue 4,
December 2023
Pages 513-525
DOI: 10.26599/BDMA.2022.9020052
Cite this article:
Kumar A, Singh KU, Kumar M. A Clinical Data Analysis Based Diagnostic Systems for Heart Disease Prediction Using Ensemble Method. Big Data Mining and Analytics, 2023, 6(4): 513-525. https://doi.org/10.26599/BDMA.2022.9020052

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Received: 29 July 2022
Revised: 10 December 2022
Accepted: 29 December 2022
Published: 29 August 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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