Comparative Analysis for Prediction of Coronary Artery Disease Using Machine Learning Algorithms

Authors

  • Ravi Hosur Department of Computer Science and Engineering, BLDEA’s V. P. Dr. P. G. Halakatti College of Engineering and Technology, Vijayapur586103, Karnataka, India
  • Pavan Mahendrakar Department of Computer Science and Engineering, BLDEA’s V. P. Dr. P. G. Halakatti College of Engineering and Technology, Vijayapur586103, Karnataka, India
  • Mahesh Nagaral Department of Computer Science and Engineering, BLDEA’s V. P. Dr. P. G. Halakatti College of Engineering and Technology, Vijayapur586103, Karnataka, India
  • Anand Hiremath Department of Computer Science and Engineering, BLDEA’s V. P. Dr. P. G. Halakatti College of Engineering and Technology, Vijayapur586103, Karnataka, India
  • Shashidhar B. Patil Department of ComputerApplications (MCA), BLDEA’s V. P. Dr. P. G. Halakatti College of Engineering and Technology, Vijayapur586103, Karnataka, India

Keywords:

Coronary Artery Disease (CAD), Naïve Bayes, Convolution Neural Network (CNN), Logistic Regression, Machine Learning, Generative Adversarial Networks, K-Nearest Neighbor (KNN)

Abstract

Cardiovascular disease, another name for heart disease, is linked to a number of conditions that affect the heart. Over the past few decades, heart disease has consistently been the leading cause of death. Numerous risk factors for heart disease are also identified, as well as the need of early disease management. This study includes a number of heart disease-related characteristics as well as models based on machine learning techniques like Nave Bayes, Convolution Neural Networks, and Logistic Regression. All previous trials relate to utilising a subset of 14, but we used the publicly accessible UCI heart disease database, which has 76 features. The purpose of this study is to estimate a patient's risk of getting heart disease. We have applied three machine learning classifiers for comparative analysis. In comparison to CNN and the Naive Bayesian algorithm, Logistic Regression has a higher accuracy of 93.22 percent.

Downloads

Download data is not yet available.

References

M. R. Ahmed, S. M. Hasan Mahmud, M. A. Hossin, H. Jahan and S. R. Haider Noori, "A Cloud Based Four-Tier 253 Architecture for Early Detection of Heart Disease with Machine Learning Algorithms," 2018 IEEE 4th International 254 Conference on Computer and Communications (ICCC), 2018, pp. 1951-1955, doi: 10.1109/CompComm.2018.8781022. 255

Asma Baccouche , Begonya Garcia-Zapirain, Cristian Castillo Olea, Adel Elmaghraby, “Ensemble Deep Learning Models 256 for Heart Disease Classification: A Case Study from Mexico”, Information 2020, 11(4), 207; 257 https://doi.org/10.3390/info11040207. 258

Y. Peili, Y. Xuezhen, Y. Jian, Y. Lingfeng, Z. Hui and L. Jimin, "Deep learning model management for coronary heart 259 disease early warning research," 2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis 260 (ICCCBDA), 2018, pp. 552-557, doi: 10.1109/ICCCBDA.2018.8386577. 261

A. Gavhane, G. Kokkula, I. Pandya and K. Devadkar, "Prediction of Heart Disease Using Machine Learn-ing," 2018 262 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2018, pp. 263 1275-1278, doi: 10.1109/ICECA.2018.8474922. 264

Md. Ashraful Alam, Mohsinul Bari Shakir, Monirul Islam Pavel, “Early detection of coronary artery blockage using 265 image processing: segmentation, quantification, identification of degree of blockage and risk factors of heart attack” 266 (Conference Presentation) May 2019 , DOI:10.1117/12.2517452, Conference: Micro- and Nanotechnology Sensors, 267 Systems, and Applications XIAt: Baltimore, Maryland, USA. 268

Vardhan Shorewala, Early detection of coronary heart disease using ensemble techniques, Informatics in Medicine 269 Unlocked, Volume 26, 2021, 100655, ISSN 2352-9148, https://doi.org/10.1016/j.imu.2021.100655. 270

Ed-daoudy, A., Maalmi, K. A new Internet of Things architecture for real-time prediction of various dis-eases using 271 machine learning on big data environment. J Big Data 6, 104 (2019). https://doi.org/10.1186/s40537-019-0271-7 272

Aravind Akella, Sudheer Akella, “Machine learning algorithms for predicting coronary artery disease: ef-forts toward an 273 open source solution”, FUTURE SCIENCE, VOL. 7, NO. 6., 29 Mar 2021https://doi.org/10.2144/fsoa-2020-0206. 274

Yar Muhammad, Muhammad Tahir, Maqsood Hayat & Kil To Chong, “Early and accurate detection and diagnosis of 275 heart disease using intelligent computational model”, Scientific Reports volume 10, Article number: 19747 (2020). 276

Chen, Xueping & Fu, Yi & Lin, Jiangguo & Ji, Yanru & Fang, Ying & Wu, Jianhua. (2020). Coronary Artery Disease 277 Detection by Machine Learning with Coronary Bifurcation Features. Applied Sciences. 10. 7656. 10.3390/app10217656. 278.

Shah, D., Patel, S. & Bharti, S.K. Heart Disease Prediction using Machine Learning Techniques. SN COMPUT. SCI. 1, 345 (2020). https://doi.org/10.1007/s42979-020-00365.

A. Singh and R. Kumar, "Heart Disease Prediction Using Machine Learning Algorithms," 2020 International Conference on Electrical and Electronics Engineering (ICE3), 2020, pp. 452-457, doi: 10.1109/ICE348803.2020.9122958.

Md Mamun Ali, Bikash Kumar Paul, Kawsar Ahmed, Francis M. Bui, Julian M.W. Quinn, Mohammad Ali Moni, Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison, Computers in Biology and Medicine, Volume 136, 2021, 104672, ISSN 0010-4825, https://doi.org/10.1016/j.compbiomed.2021.104672.

https://thenationalpilot.ng/2019/01/21/silent-ischemia-heart-disease. 279

https://www.kaggle.com/datasets/5ff0a500aa39c3c772e989cddb13bc693039f062affc01e6447599368944b7f6.280

Downloads

Published

12.01.2024

How to Cite

Hosur, R. ., Mahendrakar, P. ., Nagaral, M. ., Hiremath, A. ., & Patil, S. B. . (2024). Comparative Analysis for Prediction of Coronary Artery Disease Using Machine Learning Algorithms. International Journal of Intelligent Systems and Applications in Engineering, 12(12s), 735 –. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/4559

Issue

Vol. 12 No. 12s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.