A Machine Learning Framework for Feature Selection in Heart Disease Classification Using Improved Particle Swarm Optimization with Support Vector Machine Classifier
Abstract
Abstract Machine learning is used as an effective support system in health diagnosis which contains large volume of data. More commonly, analyzing such a large volume of data consumes more resources and execution time. In addition, all the features present in the dataset do not support in achieving the solution of the given problem. Hence, there is a need to use an effective feature selection algorithm for finding the more important features that contribute more in diagnosing the diseases. The Particle Swarm Optimization (PSO) is one of the metaheuristic algorithms to find the best solution with less time. Nowadays, PSO algorithm is not only used to select the more significant features but also removes the irrelevant and redundant features present in the dataset. However, the traditional PSO algorithm has an issue in selecting the optimal weight to update the velocity and position of the particles. To overcome this issue, this paper presents a novel function for identifying optimal weights on the basis of population diversity function and tuning function. We have also proposed a novel fitness function for PSO with the help of Support Vector Machine (SVM). The objective of the fitness function is to minimize the number of attributes and increase the accuracy. The performance of the proposed PSO-SVM is compared with the various existing feature selection algorithms such as Info gain, Chi-squared, One attribute based, Consistency subset, Relief, CFS, Filtered subset, Filtered attribute, Gain ratio and PSO algorithm. The SVM classifier is also compared with several classifiers such as Naive Bayes, Random forest and MLP.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The accuracy of machine learning classifiers for heart disease diagnosis has been found to be low.
“However, the accuracy of such classifiers has been found to be low.”
From A healthcare monitoring system for the diagnosis of heart disease in the IoMT cloud environment using MSSO-ANFIS (2020) · cites this paper as [16]
Cites
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
Arabasadi, Z., Alizadehsani, R., Roshanzamir, M., Moosaei, H., & Yarifard, A. A. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.01.004 · Cited by 499 · 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
Ömürlü, İ. K., Türe, M., & Kurum, A. T. (2006) · Expert Systems with Applications · doi:10.1016/j.eswa.2006.09.004 · Cited by 406 · see citing works
Association rule mining to detect factors which contribute to heart disease in males and females
Nahar, J., Imam, T., Tickle, K. S., & Chen, Y.-P. P. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.08.028 · Cited by 394 · see citing works
Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules
Anooj, P. (2011) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2011.09.002 · Cited by 384 · see citing works
Austin, P. C., Tu, J. V., Ho, J. E., Levy, D. A., & Lee, D. S. (2013) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2012.11.008 · Cited by 340 · see citing works
Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis
Inbarani, H. H., Azar, A. T., & Ganesan, J. (2013) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2013.10.007 · Cited by 307 · see citing works
Özçift, A., & Gülten, A. (2011) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2011.03.018 · Cited by 299 · see citing works
Li, Q., Chen, H., Huang, H., Zhao, X., Cai, Z., Tong, C., Liu, W., & Tian, X. (2017) · Computational and Mathematical Methods in Medicine · doi:10.1155/2017/9512741 · Cited by 286 · see citing works
Computational intelligence for heart disease diagnosis: A medical knowledge driven approach
Nahar, J., Imam, T., Tickle, K. S., et al. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.07.032 · Cited by 267 · see citing works
Association Rule Discovery With the Train and Test Approach for Heart Disease Prediction
ORDÓÑEZ, C. R. (2006) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2006.864475 · Cited by 212 · see citing works
Feature selection for medical diagnosis : Evaluation for cardiovascular diseases
Shilaskar, S., & Ghatol, A. A. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.01.032 · Cited by 207 · see citing works
Integrating data mining with case-based reasoning for chronic diseases prognosis and diagnosis
Huang, M.-J., Chen, M., & Lee, S.-C. (2006) · Expert Systems with Applications · doi:10.1016/j.eswa.2006.01.038 · Cited by 189 · see citing works
Babaoğlu, İ., Fındık, O., & Ülker, E. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.09.064 · Cited by 189 · see citing works
Automated Diagnosis of Coronary Artery Disease Based on Data Mining and Fuzzy Modeling
Tsipouras, M. G., Exarchos, T. P., Fotiadis, D. I., Kotsia, A. P., Vakalis, K., Naka, K. Κ., & Michalis, L. K. (2008) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2007.907985 · Cited by 189 · see citing works
Zhao, M., Fu, C., Ji, L., Tang, K., & Ming-tian, Z. (2010) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.10.041 · Cited by 186 · see citing works
Vivekanandan, T., & Sriman Narayana Iyengar, N. C. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.09.011 · Cited by 185 · see citing works
Polat, K., & Güneş, S. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.01.041 · Cited by 182 · see citing works
Muthukaruppan, S., & Er, M. J. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.04.036 · Cited by 173 · see citing works
Shah, S. M. S., Batool, S., Khan, I., Ashraf, M. U., Abbas, S. H., & Hussain, S. A. (2017) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/j.physa.2017.04.113 · Cited by 150 · see citing works
A hybrid evolutionary algorithm for attribute selection in data mining
Tan, K. C., Teoh, E. J., Yu, Q., et al. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.10.013 · Cited by 146 · see citing works
Polat, K., Şahan, S., & Güneş, S. (2006) · Expert Systems with Applications · doi:10.1016/j.eswa.2006.01.027 · Cited by 135 · see citing works
Eom, J.-H., Kim, S., & ZHANG, B. (2007) · Expert Systems with Applications · doi:10.1016/j.eswa.2007.04.015 · Cited by 126 · see citing works
Fuzzy expert system approach for coronary artery disease screening using clinical parameters
Pal, D., Mandana, K. M., Pal, S., Sarkar, D., & Chakraborty, C. (2012) · Knowledge-Based Systems · doi:10.1016/j.knosys.2012.06.013 · Cited by 124 · see citing works
Son, C., Kim, Y.-N., Kim, H., et al. (2012) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2012.04.013 · Cited by 117 · see citing works
Diagnosis of valvular heart disease through neural networks ensembles
Daş, R., Türkoğlu, İ., & Şengür, A. (2008) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2008.09.005 · Cited by 115 · see citing works
Gadekallu, T. R., & Khare, N. (2016) · Journal of Circuits Systems and Computers · doi:10.1142/s021812661750061x · Cited by 114 · see citing works
Goncalves, L. B., Vellasco, M. M. B. R., Pacheco, M. A. C., et al. (2006) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2004.843220 · Cited by 111 · see citing works
Genetic algorithm based fuzzy decision support system for the diagnosis of heart disease
Paul, A. K., Shill, P. C., Rabin, M. R. I., & Akhand, M. A. H. (2016) · International Conference on Informatics, Electronics & Vision (ICIEV) · doi:10.1109/iciev.2016.7759984 · Cited by 94 · see citing works
Feature Selection based Least Square Twin Support Vector Machine for Diagnosis of Heart Disease
Tomar, D., & Agarwal, S. (2014) · International Journal of Bio-Science and Bio-Technology · doi:10.14257/ijbsbt.2014.6.2.07 · Cited by 92 · see citing works
A fuzzy-evidential hybrid inference engine for coronary heart disease risk assessment
Khatibi, V., & Montazer, G. A. (2010) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.05.022 · Cited by 88 · see citing works
Polat, K., & Güneş, S. (2007) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2007.07.013 · Cited by 80 · see citing works
Yan, H., Zheng, J. J., Jiang, Y., Peng, C., & Xiao, S. (2007) · Applied Soft Computing · doi:10.1016/j.asoc.2007.05.017 · Cited by 66 · see citing works
Prediction of Heart Disease Using Random Forest and Feature Subset Selection
Jabbar, M. A., Deekshatulu, B. L., & Chandra, P. (2015) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-28031-8_16 · Cited by 66 · see citing works
Iftikhar, S., Fatima, K., Rehman, A. U., Almazyad, A. S., & Saba, T. (2017) · Biomedical Research-tokyo · Cited by 64 · see citing works
Feature Selection in Ischemic Heart Disease Identification using Feed Forward Neural Networks
Rajeswari, K., Vaithiyanathan, V., & Neelakantan, T. R. (2012) · Procedia Engineering · doi:10.1016/j.proeng.2012.08.109 · Cited by 45 · see citing works
Cost-sensitive case-based reasoning using a genetic algorithm: Application to medical diagnosis
Park, Y., Chun, S., & Kim, B.-C. (2011) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2010.12.001 · Cited by 44 · see citing works
Babaoğlu, İ., Baykan, Ö. K., Aygül, N., et al. (2007) · Expert Systems with Applications · doi:10.1016/j.eswa.2007.11.013 · Cited by 43 · see citing works
A decision support system for cost-effective diagnosis
Chi, C., Street, W. N., & Katz, D. A. (2010) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2010.08.001 · Cited by 39 · see citing works
Luukka, P., & Lampinen, J. (2010) · Adaptation, learning, and optimization · doi:10.1007/978-3-642-12775-5_11 · Cited by 38 · see citing works
Investigation on Cardiovascular Risk Prediction Using Genetic Information
Pu, L., Zhao, Z., & Zhang, Y. (2012) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2012.2205009 · Cited by 36 · see citing works
Innovations in Bio-inspired Computing and Applications
Abraham, A. 1., Krömer, P., & Snåšel, V. (2013) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-01781-5 · Cited by 32 · see citing works
Karsdorp, P. A., Kindt, M., Rietveld, S., Everaerd, W. T. A. M., & Mulder, B. J. M. (2009) · International Journal of Behavioral Medicine · doi:10.1007/s12529-008-9001-9 · Cited by 30 · see citing works
Nawi, N. M., Ghazali, R., & Salleh, M. N. M. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-16167-4_41 · Cited by 29 · see citing works
Turan, R. G., Bozdag‐T, I., Ortak, J., et al. (2010) · Stem Cell Reviews and Reports · doi:10.1007/s12015-010-9220-8 · Cited by 25 · see citing works
Pfister, R., Barnes, D. R., Luben, R., Khaw, K., Wareham, N. J., & Langenberg, C. (2011) · Diabetologia · doi:10.1007/s00125-011-2206-5 · Cited by 24 · see citing works
Long term cardiovascular risk models’ combination
Paredes, S., Rocha, T., Carvalho, P., et al. (2011) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2010.12.015 · Cited by 23 · see citing works
Ubiquitous Machine Learning and Its Applications
(2017) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-5225-2545-5 · Cited by 12 · see citing works
Setiawan, N. A., Venkatachalam, P. A., & Hani, A. F. M. (2008) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-540-69139-6_69 · Cited by 11 · see citing works
Pimentel, A., Gamboa, H. F. S., Almeida, I. M., Matos, P., Ribeiro, R. T., & Raposo, J. F. (2018) · IGI Global eBooks · doi:10.4018/978-1-5225-7122-3.ch011 · Cited by 2 · see citing works
Pimentel, A., Gamboa, H. F. S., Almeida, I. M., Matos, P., Ribeiro, R. T., & Raposo, J. F. (2017) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-5225-2545-5.ch005 · Cited by 1 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Khan, M. A., & Algarni, F. (2020) · IEEE Access · doi:10.1109/access.2020.3006424 · Cited by 283 · see citing works
- Authors
- Vijayashree, J., & Sultana, H. P.
- Affiliations
- Vellore Institute of Technology University (IN)
- Corresponding author
- Vijayashree, J.
- Venue
- Programming and Computer Software
- Publisher
- Pleiades Publishing Ltd
- Published
- 2018-11
- Volume
- 44
- Issue
- 6
- Pages
- 388-397
- ISSN
- 0361-7688 (ISSN-L)
- 1608-3261
- OpenAlex
- W2922256990
- Cited by
- 130
- References
- 52
- Citations by year
- 2026: 12 · 2025: 14 · 2024: 27 · 2023: 22 · 2022: 25 · 2021: 19 · 2020: 7 · 2019: 4
- FWCI
- 9.52
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Programming and Computer Software (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
- Keywords
- feature selection, heart disease classification, particle swarm optimization, support vector machine, fitness function, metaheuristic optimization, dimensionality reduction, diagnosis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
