A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms
Open accessAbstract
Heart disease is one of the most critical human diseases in the world and affects human life very badly. In heart disease, the heart is unable to push the required amount of blood to other parts of the body. Accurate and on time diagnosis of heart disease is important for heart failure prevention and treatment. The diagnosis of heart disease through traditional medical history has been considered as not reliable in many aspects. To classify the healthy people and people with heart disease, noninvasive-based methods such as machine learning are reliable and efficient. In the proposed study, we developed a machine-learning-based diagnosis system for heart disease prediction by using heart disease dataset. We used seven popular machine learning algorithms, three feature selection algorithms, the cross-validation method, and seven classifiers performance evaluation metrics such as classification accuracy, specificity, sensitivity, Matthews’ correlation coefficient, and execution time. The proposed system can easily identify and classify people with heart disease from healthy people. Additionally, receiver optimistic curves and area under the curves for each classifier was computed. We have discussed all of the classifiers, feature selection algorithms, preprocessing methods, validation method, and classifiers performance evaluation metrics used in this paper. The performance of the proposed system has been validated on full features and on a reduced set of features. The features reduction has an impact on classifiers performance in terms of accuracy and execution time of classifiers. The proposed machine-learning-based decision support system will assist the doctors to diagnosis heart patients efficiently.
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There are fewer studies that use hybrid metaheuristic techniques for heart disease prediction.
“However, there are fewer studies [25], [29], [30] which use hybrid metaheuristic techniques.”
From A healthcare monitoring system for the diagnosis of heart disease in the IoMT cloud environment using MSSO-ANFIS (2020) · cites this paper as [30]
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Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918 · see citing works
Peng, H., Long, F., & Ding, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.159 · Cited by 10,614 · see citing works
Statistical pattern recognition: a review
Jain, A. K., Duin, P., & Mao, J. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.824819 · Cited by 6,795 · see citing works
Friedman, N., Geiger, D., & Goldszmidt, M. (1997) · Machine Learning · doi:10.1023/a:1007465528199 · Cited by 4,742 · see citing works
Regression Shrinkage and Selection via The Lasso: A Retrospective
Tibshirani, R. J. (2011) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2011.00771.x · Cited by 3,601 · see citing works
Heidenreich, P. A., Trogdon, J. G., Khavjou, O. A., Butler, J., Dracup, K., Ezekowitz, M. D., Finkelstein, E. A., Hong, Y., Johnston, S. C., Khera, A., et al. (2011) · Circulation · doi:10.1161/cir.0b013e31820a55f5 · Cited by 3,208 · see citing works
Epidemiology and risk profile of heart failure
Bui, A. L., Horwich, T. B., & Fonarow, G. C. (2011) · Nature Reviews Cardiology · doi:10.1038/nrcardio.2010.165 · Cited by 2,077 · see citing works
Relief-based feature selection: Introduction and review
Urbanowicz, R. J., Meeker, M., La Cava, W., Olson, R. S., & Moore, J. H. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.07.014 · Cited by 1,311 · see citing works
Detrano, R., Janosi, A., Steinbrunn, W., Pfisterer, M. E., Schmid, J.-J., Sandhu, S. S., Guppy, K. H., Lee, S. J., & Froelicher, V. F. (1989) · The American Journal of Cardiology · doi:10.1016/0002-9149(89)90524-9 · Cited by 705 · see citing works
Mourão-Miranda, J., Bokde, A. L. W., Born, C., Hampel, H. J., & Stetter, M. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.06.070 · Cited by 700 · see citing works
Effective diagnosis of heart disease through neural networks ensembles
Das, R., Turkoglu, I., & Sengur, A. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.09.013 · Cited by 628 · see citing works
Interpreting Parameters in the Logistic Regression Model with Random Effects
Larsen, K. R., Petersen, J. H., Budtz–Jørgensen, E., & Endahl, L. (2000) · Biometrics · doi:10.1111/j.0006-341x.2000.00909.x · Cited by 474 · see citing works
An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction
Samuel, O. W., Asogbon, G. M., Sangaiah, A. K., Fang, P., & Li, G. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.10.020 · Cited by 425 · see citing works
Design of a hybrid system for the diabetes and heart diseases
Kahramanlı, H., & Allahverdi, N. (2007) · Expert Systems with Applications · doi:10.1016/j.eswa.2007.06.004 · Cited by 418 · see citing works
Chen, H., Yang, B., Liu, J., & Liu, D. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.01.120 · Cited by 383 · see citing works
Advanced support vector machines and kernel methods
V.David Sánchez, A. (2003) · Neurocomputing · doi:10.1016/s0925-2312(03)00373-4 · Cited by 368 · see citing works
Ünler, A., Murat, A., & Chinnam, R. B. (2010) · Information Sciences · doi:10.1016/j.ins.2010.05.037 · Cited by 325 · see citing works
Heart Diseases Diagnosis Using Neural Networks Arbitration
Olaniyi, E. O., Oyedotun, O. K., & Khashman, A. (2015) · International Journal of Intelligent Systems and Applications · doi:10.5815/ijisa.2015.12.08 · Cited by 150 · see citing works
Vanisree, K. L., & Singaraju, J. (2011) · International Journal of Computer Applications · doi:10.5120/2368-3115 · Cited by 118 · see citing works
Innovative Artificial Neural Networks-Based Decision Support System for Heart Diseases Diagnosis
Ghwanmeh, S., Mohammad, A. H., & Al-Ibrahim, A. M. H. (2013) · Journal of Intelligent Learning Systems and Applications · doi:10.4236/jilsa.2013.53019 · Cited by 97 · see citing works
Birthmark-Based Software Classification Using Rough Sets
Nazir, S., Shahzad, S., & Riza, L. S. (2016) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-016-2371-4 · Cited by 34 · see citing works
Self-citationClustering based on correlation fractal dimension over an evolving data stream.
Yarlagadda, A., Jonnalagedda, M. V., & Munaga, K. (2018) · The International Arab Journal of Information Technology · Cited by 22 · see citing works
Flagged first, then by citations
Research works printed but not linked: 18
- M. Durairaj and N. Ramasamy, "A comparison of the per- ceptive approaches for preprocessing the data set for pre- dicting fertility success rate," International Journal of Control eory and Applications, vol. 9, pp. 256-260, 2016.
- Q. K. Al-Shayea, "Artificial neural networks in medical di- agnosis," International Journal of Computer Science Issues, vol. 8, no. 2, pp. 150-154, 2011.
- J. López-Sendón, " e heart failure epidemic," Medi- cographia, vol. 33, pp. 363-369, 2011.
- S. Nazir, S. Shahzad, S. Mahfooz, and M. Nazir, "Fuzzy logic based decision support system for component security evaluation," International Arab Journal of Information Technology, vol. 15, pp. 1-9, 2015.
- A. Methaila, P. Kansal, H. Arya, and P. Kumar, "Early heart disease prediction using data mining techniques," in Proceedings of Computer Science & Information Technology (CCSIT-2014), vol. 24, pp. 53-59, Sydney, NSW, Australia, 2014.
- B. Edmonds, "Using localised 'Gossip' to structure distributed learning," in Proceedings of AISB symposium on Socially In- spired Computing, pp. 1-12, Hatfield, UK, April 2005.
- M. Gudadhe, K. Wankhade, and S. Dongre, "Decision support system for heart disease based on support vector machine and artificial neural network," in Proceedings of International Conference on Computer and Communication Technology (ICCCT), pp. 741-745, Allahabad, India, September 2010.
- S. Palaniappan and R. Awang, "Intelligent heart disease prediction system using data mining techniques," in Pro- ceedings of IEEE/ACS International Conference on Computer Systems and Applications (AICCSA 2008), pp. 108-115, Doha, Qatar, March-April 2008.
- M. A. Jabbar, B. L. Deekshatulu, and P. Chandra, "Classifi- cation of heart disease using artificial neural network and feature subset selection," Global Journal of Computer Science and Technology Neural & Artificial Intelligence, vol. 13, no. 11, 2013.
- A. M. D. Silva, Feature Selection, vol. 13, pp. 1-13, Springer, Berlin, Germany, 2015.
- R. Gilad-Bachrac, A. Navot, and N. Tishby, "Margin based feature selection-theory and algorithms," in Proceedings of the Twenty-First International Conference on Machine Learning, p. 43, Banff, AB, Canada, July 2004.
- F. E. Harrell, "Ordinal logistic regression," in Regression Modeling Strategies, pp. 311-325, Springer, Berlin, Germany, 2015.
- V. Vapnik, e Nature of Statistical Learning eory, Springer Science & Business Media, Berlin, Germany, 2013.
- N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University Press, Cambridge, UK, 2000.
- X. Wu and V. Kumar, Top 10 Algorithms in Data Mining, Springer, Berlin, Germany, 2007.
- A. K. Jain, P. W. Duin, and J. Mao, "Statistical pattern rec- ognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 1, pp. 4-37, 1999.
- P. W. Wagacha, "Induction of decision trees," Foundations of Learning and Adaptive Systems, vol. 12, pp. 1-14, 2003.
- A. M. De Silva and P. H. W. Leong, Grammar-Based Feature Generation for Time-Series Prediction, Springer, Berlin, Ger- many, 2015.
Cited by
Sorted by citations
Kumar, Y., Koul, A., Singla, R., & Ijaz, M. F. (2023) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03612-z · Cited by 1,040 · see citing works
RetractedKhan, M. A., & Algarni, F. (2020) · IEEE Access · doi:10.1109/access.2020.3006424 · Cited by 283 · see citing works
AI Advances in ICU with an Emphasis on Sepsis Prediction: An Overview
Stylianides, C., Nicolaou, A., Sulaiman, W. A., Alexandropoulou, C.-A., Panagiotopoulos, I., Karathanasopoulou, K., Dimitrakopoulos, G., Kleanthous, S., Politi, E., Ntalaperas, D., et al. (2025) · Machine Learning and Knowledge Extraction · doi:10.3390/make7010006 · Cited by 28 · see citing works
Mishra, J., Tiwari, M., Singh, S. T., & Goswami, S. (2022) · 2021 4th International Conference on Recent Trends in Computer Science and Technology (ICRTCST) · doi:10.1109/icrtcst54752.2022.9782009 · Cited by 4 · see citing works
- Authors
- Haq, A. U., Li, J. P., Memon, M. H., Nazir, S., & Sun, R.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
- University of Swabi (PK)
- Corresponding author
- Haq, Amin Ul
- Venue
- Mobile Information Systems
- Publisher
- Wiley
- Published
- 2018-12-02
- Volume
- 2018
- Pages
- 1-21
- ISSN
- 1574-017X (ISSN-L)
- 1875-905X
- OpenAlex
- W2903099708
- Cited by
- 621
- References
- 22
- Citations by year
- 2026: 25 · 2025: 60 · 2024: 95 · 2023: 128 · 2022: 132 · 2021: 101 · 2020: 63 · 2019: 16 · 2018: 1
- FWCI
- 57.46
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Mobile Information Systems (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Natural Science Foundation of China (61370073, 2007AA01Z423)
- National High Technology Research and Development Program of China (61370073, 2007AA01Z423)
- Department of Science and Technology of Sichuan Province (61370073, 2007AA01Z423)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Keywords
- heart disease prediction, heart disease diagnosis, hybrid intelligent systems, feature selection, feature reduction, classifier evaluation, receiver operating characteristic, decision support system, non-invasive diagnosis, heart disease dataset
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