Proceedings of the Second International Conference on Emerging Trends in Engineering (ICETE 2023)

<Previous Article In Volume
Next Article In Volume>

Heart Disease Prediction with Ensemble Learning Technique

Authors
R. Santosh1, *, B. M. M. Tripathi1, Arempula Sreenivasa Rao2, Y. Satwik3
1Department of Electronics and Communication Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Kanuru, Vijayawada, 520007, India
2Department of Electronics and Communication Engineering, Annamacharya Institute of Technology and Sciences, Hyderabad, 501512, India
3Department of Computer Sciecnce and Engineering, CVR College of Engineering, Hyderabad, 501510, India
*Corresponding author. Email: routus@gmail.com
Corresponding Author
R. Santosh
Available Online 9 November 2023.
DOI
10.2991/978-94-6463-252-1_11How to use a DOI?
Keywords
Machine Learning; Classification; Heart Disease Prediction; Python
Abstract

Machine Learning (ML) is a field of science which is proven to be significantly effective and efficient in forecasting diseases and making predictions from analysing the enormous amounts of data produced by various healthcare industries. Several engineers across the world have developed ML algorithms for heart disease prediction in which different accuracies are obtained for the same technique for a given data set. It is in reality, contradictory to say which algorithm will be more beneficial to predicting whether the heart is healthy or unhealthy. A novel approach has been presented to predicting heart disease in which six algorithms have been developed and analyzed for predicting the heart disease efficiently. The automatic and efficient output will be derived depending on the accuracy, sensitivity, specificity, and precision. The Random Forest and Naïve Baye’s classifier have proven effective in predicting heart disease from the UCI Cleveland dataset.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the Second International Conference on Emerging Trends in Engineering (ICETE 2023)
Series
Advances in Engineering Research
Publication Date
9 November 2023
ISBN
10.2991/978-94-6463-252-1_11
ISSN
2352-5401
DOI
10.2991/978-94-6463-252-1_11How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - R. Santosh
AU  - B. M. M. Tripathi
AU  - Arempula Sreenivasa Rao
AU  - Y. Satwik
PY  - 2023
DA  - 2023/11/09
TI  - Heart Disease Prediction with Ensemble Learning Technique
BT  - Proceedings of the Second International Conference on Emerging Trends in Engineering (ICETE 2023)
PB  - Atlantis Press
SP  - 87
EP  - 95
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-252-1_11
DO  - 10.2991/978-94-6463-252-1_11
ID  - Santosh2023
ER  -