Heart Attack Prediction Using Machine Learning

Year : 2023 | Volume : 01 | Issue : 01 | Page : 8 13
    By

    Sumit Halder,

  • Amit Chakraborty,

  • Saswati Naskar,

  • Saibal Majumder,

  1. Student, Department of Computer Science and Engineering, Greater Kolkata College of Engineering and Management (GKCEM), Kolkata, India
  2. Student, Department of Computer Science and Engineering, Greater Kolkata College of Engineering and Management (GKCEM),, Kolkata, India
  3. Assistant Professor, Department of Computer Science and Engineering, Greater Kolkata College of Engineering and Management (GKCEM),, Kolkata, India
  4. Assistant Professor, Department of Computer Science and Engineering, Greater Kolkata College of Engineering and Management (GKCEM),, Kolkata, India

Abstract

Heart attacks have become a prevalent and serious condition in recent years due to a variety of causes. Numerous variables, including age, sex, fat, and others, can be used to predict it. In the current study, it was found that a data set with 13 parameters and 302 distinct data values, collected from a Kaggle dataset to assess patient condition, was covered. This article delves into the application of machine learning and artificial intelligence algorithms for the prevention of heart disease. The primary objective of this study is to enhance the accuracy and precision of identifying cardiac ailments by utilizing advanced algorithms. These algorithms play a pivotal role in discerning the presence or absence of cardiac issues within a given case. By harnessing the power of these technologies, the article aims to revolutionize cardiac complaint detection, ultimately contributing to early intervention and improved patient outcomes in the realm of heart disease management.

Keywords: Artificial Intelligence, Machine Learning, Regression, SVM, Logistic Regression

[This article belongs to International Journal of Biomedical Innovations and Engineering ]

How to cite this article:
Sumit Halder, Amit Chakraborty, Saswati Naskar, Saibal Majumder. Heart Attack Prediction Using Machine Learning. International Journal of Biomedical Innovations and Engineering. 2023; 01(01):8-13.
How to cite this URL:
Sumit Halder, Amit Chakraborty, Saswati Naskar, Saibal Majumder. Heart Attack Prediction Using Machine Learning. International Journal of Biomedical Innovations and Engineering. 2023; 01(01):8-13. Available from: https://journals.stmjournals.com/ijbie/article=2023/view=117710


References

  1. Palaniappan S, Awang R. Intelligent heart disease prediction system using data mining techniques. 2008 IEEE/ACS International Conference on Computer Systems and Applications; 2008 March 31–April 4; Doha, Qatar. IEEE; 2008. p. 108-115. doi:10.1109/AICCSA.2008.4493524.

  2. Nikan S, Gwadry-Sridhar F, Bauer M. Machine learning application to predict the risk of coronary artery atherosclerosis. 2016 International Conference on Computational Science and Computational Intelligence (CSCI); 2016 Dec 15-17; Las Vegas, NV, USA. IEEE; 2016. p. 34-39. doi:10.1109/CSCI.2016.0014.

  3. Bashir S, Qamar U, Younus Javed M. An ensemble-based decision support framework for intelligent heart disease diagnosis. International Conference on Information Society (i-Society 2014); 2014 Nov 10-12; London, UK. IEEE; 2014. p. 259-264. doi:10.1109/i-Society.2014.7009056.

  4. Ramalingam VV, Dandapath A, Raja MK. Heart disease prediction using machine learning techniques: a survey. Int J Eng Technol. 2018;7(2):684-7.

  5. Chapman P, Clinton J, Kerber R, Khabeza T, Reinartz T, Shearer C, Wirth R. CRISP-DM 1.0: Step-by-step data mining guide. SPSS; 2000.

  6. Charly K. Data mining for the enterprise. Proceedings of the 31st Annual Hawaii International Conference on System Sciences; 1998 Jan 6-9; Hawaii, USA. IEEE; 1998. p. 295-304.

  7. Fayyad U. Data mining and knowledge discovery in databases: implications for scientific databases. Proceedings of the 9th International Conference on Scientific and Statistical Database Management; 1997 Aug 11-13; Olympia, Washington, USA. IEEE; 1997. p. 2-11.

  8. Giudici P. Applied data mining: statistical methods for business and industry. Faculty of Economics, University of Pavia, Italy [Internet]. Docplayer.net; 2013 [cited 2023 Aug 7]. Available from: https://docplayer.net/16673861-Applied-data-mining-statistical-methods-for-business-and-industry-paolo-giudici-faculty-of-economics-university-of-pavia-italy.html.

  9. Han J, Kamber M. Data Mining: Concepts and Techniques. 2nd ed. San Francisco, CA: Morgan Kaufmann; 2006. Available from: https://hanj.cs.illinois.edu/bk3/bk3_slidesindex.htm.

  10. Mohd D, Awang R. Intelligent heart disease prediction system using data mining techniques [Internet]. ResearchGate. 2008 [cited 2023 Aug 7]. Available from: https://www.researchgate.net/publication/4329399_Intelligent_heart_disease_prediction_system_using_data_mining_techniques.

  11. Kaur H, Wasan SK. Empirical study on applications of data mining techniques in healthcare. J Comput Sci. 2006;2(2):194-200.

Regular Issue Subscription Original Research
Volume 01
Issue 01
Received 13/07/2023
Accepted 30/07/2023
Published 10/08/2023
Publication Time 28 Days
946

Login


My IP

PlumX Metrics