A New Ensenble Learning based Optimal Prediction Model for Cardiovascular Diseases

B. Srınıvasa Rao

doi:10.1051/e3sconf/202130901007

All issues

Volume 309 (2021)

E3S Web Conf., 309 (2021) 01007

References

Open Access

Issue		E3S Web Conf. Volume 309, 2021 3^rd International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED-ICMPC 2021)


Article Number		01007
Number of page(s)		8
DOI		https://doi.org/10.1051/e3sconf/202130901007
Published online		07 October 2021

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[1] Chauhan, A., Jain, A., Sharma, P., and Deep, V. “Heart disease prediction using evolutionary rule learning.” (4th International Conference on Computational Intelligence & Communication Technology (CICT), IEEE, 1 2018). [Google Scholar]

[2] Fu, J., Sun, J., and Wang, K. “Spark–a big data processing platform for ma-chine learning.” (Industrial Informatics-Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII), IEEE, 48 2016). [Google Scholar]

[3] Gnaneswar, B. and Jebarani, M. E. “A review on prediction and diagnosis of heart failure.” (International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), IEEE, 1 2017). [Google Scholar]

[4] Hasan, S., Mamun, M., Uddin, M., and Hossain, M. “Comparative analysis of classification approaches for heart disease prediction.” (International Confer-ence on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), IEEE, 1, 2018). [Google Scholar]

[5] Jabbar, M. and Samreen, S. “Heart disease prediction system based on hidden naïve bayes classifier.” (International Conference on Circuits, Controls, Commu-nications and Computing (I4C), IEEE, 1 2016). [Google Scholar]

[6] Kuo, M.-H., Chrimes, D., Moa, B., and Hu, W. “Design and construction of a big data analytics framework for health applications.” (Smart City/SocialCom/Sustain-Com, IEEE, 631 2015). [Google Scholar]

[7] Meena, G., Chauhan, P. S., and Choudhary, R. R. “Empirical study on clas-sification of heart disease dataset-its prediction and mining.” (International Con-ference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC), IEEE, 1041 2017). [Google Scholar]

[8] Prabakaran, N. and Kannadasan, R. “Prediction of cardiac disease based on pa-tient’s symptoms.” (Second International Conference on Inventive Communication and Computational Technologies (ICICCT), IEEE, 794 2018). [Google Scholar]

[9] Saxena, K., Sharma, R., et al. Pro-cedia Computer Science, 85, 962 (2016). [Google Scholar]

[10] Selvakumar, P. and Rajagopalan, S. “SshâATstructure risk minimization based support vector machine for heart disease prediction.” (2nd International Confer-ence on Communication and Electronics Systems (ICCES), IEEE, 84 2017). [Google Scholar]

[11] Sun, J. and Reddy, C. K. “Big data analytics for healthcare.” (Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 1525 2013). [Google Scholar]

[12] Suvarna, C., Sali, A., and Salmani, S. “Efficient heart disease prediction system using optimization technique.” (International Conference on Computing Method-ologies and Communication (ICCMC), IEEE, 374 2017). [Google Scholar]

[13] Vijayashree, J. and SrimanNarayanaIyengar, N. C. International Journal of Bio-Science and Bio-Technology, 8, 139 (2016). [Google Scholar]

[14] Zolfaghar, K., Meadem, N., Teredesai, A., Roy, S. B., Chin, S.-C., and Muckian, B. “Big data solutions for predicting risk-of-readmission for congestive heart fail-ure patients.” (Big Data, IEEE, 64 2013). [Google Scholar]