Skip to main content
Book cover

International Conference on Soft Computing and Data Mining

SCDM 2022: Recent Advances in Soft Computing and Data Mining pp 160–170Cite as

Deep Learning GRU Model and Random Forest for Screening Out Key Attributes of Cardiovascular Disease

  • 59 Accesses

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 457)

Abstract

The World Health organization estimates that heart disease is liable for the death of 12 million people per year throughout the world. Nearly half of all deaths are caused by cardiovascular disease each year. The earlier cardiovascular disease may be detected and treated, the lower the risk of significant outcomes for individuals who are already at risk. Developing a better heart disease prediction model can be extremely beneficial in predicting cardiovascular disease risk more accurately. Authors developed Gated Recurrent Unit (GRU) and a random forest (RF) based heart disease prediction model in this study to weed out risk factors for the heart disease. After screening out the main attributes, the proposed approach GRU-RF is tested against traditional Deep Neural Network, and K-nearest neighbour algorithms. The prediction accuracy of proposed approach is 87%, and then, finally, authors selected the best accurate algorithm to develop a model for the prediction of heart disease in the future.

Keywords

  • Machine learning
  • Deep learning
  • Heart disease
  • GRU
  • Random forest
  • Deep neural network
  • K-nearest neighbour

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-031-00828-3_16
  • Chapter length: 11 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   229.00
Price excludes VAT (USA)
  • ISBN: 978-3-031-00828-3
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book
USD   299.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.

References

  1. Global Status Report on Non-communicable Diseases [EB/OL]. https://wenku.baidu.com/view/4be69df2590216fc700abb68a9-8271fe900eaf61.html. Accessed 12 Sept 2021

  2. Yang, Q., Cogswell, M.E., Flanders, W.D., Hong, Y., Zhang, Z., et al.: Trends in cardiovascular health metrics and associations with all-cause and CVD mortality among US adults. JAMA 307, 1273–1283 (2012)

    CrossRef  Google Scholar 

  3. Zeng, Q., Dong, S.Y., Song, Z.Y., Zheng, Y.S., Wu, H.Y., et al.: Ideal cardiovascular health in Chinese urban population. Int. J. Cardiol. 167, 2311–2317 (2012)

    CrossRef  Google Scholar 

  4. Wang, J., Li, X.: A novel method of prediction for heart disease based on convolution neural networks. J. Nat. Sci. Heilongjiang Univ. 36(01), 115–120 (2019)

    Google Scholar 

  5. Li, X.: Research on the heart disease prediction method based on convolutional neural network. Northeast Forestry University, Harbin (2018)

    Google Scholar 

  6. Dileep, N.. Logistic regression to predict heart disease [EB/OL]. https://www.kaggle.com/dileep070/heart-disease-prediction-using-logistic-regression

  7. Javid, I., Zager, A., Ghazali, R.: Enhanced accuracy of heart disease prediction using machine learning and recurrent neural networks ensemble majority voting method. Int. J. Adv. Comput. Sci. Appl. 11 (2020). https://doi.org/10.14569/IJACSA.2020.0110369

  8. Rana, R.K.: Gated recurrent unit (GRU) for emotion classification from noisy speech. ArXiv, abs/1612.07778 (2016)

    Google Scholar 

  9. Wu, K.: A sentimental analysis system for film review based on deep learning. J. Electron. Res. Appl. 3 (2019). https://doi.org/10.26689/jera.v3i5.992

  10. Yokoyama, A., Yamaguchi, N.: Optimal hyperparameters for random forest to predict leakage current alarm on premises. In: E3S Web of Conferences, vol. 152, p. 03003 (2020). https://doi.org/10.1051/e3sconf/202015203003

  11. Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling (2014). https://arxiv.org/abs/1412.3555

  12. Folsom, A.R., Yatsuya, H., Nettleton, J.A., Lutsey, P.L., Cushman, M., et al., ARIC Study Investigators: Community prevalence of ideal cardiovascular health, by the American Heart Association definition, and relationship with cardiovascular disease incidence. J. Am. Coll. Cardiol. 57, 1690–1696 (2011)

    Google Scholar 

  13. Zulqarnain, M., Ghazali, R., Ghouse, M.G., Mushtaq, M.F.: Efficient processing of GRU based on word embedding for text classification. JOIV Int. J. Inform. Vis. 3(4), 377–383 (2019)

    Google Scholar 

  14. Wahid, F., Ghazali, R., Ismail, L.H.: Improved firefly algorithm based on genetic algorithm operators for energy efficiency in smart buildings. Arab. J. Sci. Eng. 44(4), 4027–4047 (2019)

    CrossRef  Google Scholar 

Download references

Acknowledgment

This research was supported by the Universiti Tun Hussein Onn Malaysia (UTHM) through the Multidisciplinary Research Grant (MDR) (Vote H494).

Author information

Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Irfan Javid & Rozaida Ghazali

  2. Department of Computer Science and IT, University of Poonch Rawalakot, Rawalakot, AJK, Pakistan

    Irfan Javid

  3. Department of Computer Science and Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan

    Muhammad Zulqarnain

  4. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Noor Aida Husaini

Authors
  1. Irfan Javid
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rozaida Ghazali
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Muhammad Zulqarnain
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Noor Aida Husaini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Irfan Javid .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Prof. Dr. Rozaida Ghazali

  2. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Prof. Dr. Nazri Mohd Nawi

  3. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Prof. Dr. Mustafa Mat Deris

  4. School of Information Technology Faculty of Science, Engineering and Built Environment, Deakin University, Geelong, VIC, Australia

    Prof. Jemal H. Abawajy

  5. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Dr. Nureize Arbaiy

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Javid, I., Ghazali, R., Zulqarnain, M., Husaini, N.A. (2022). Deep Learning GRU Model and Random Forest for Screening Out Key Attributes of Cardiovascular Disease. In: Ghazali, R., Mohd Nawi, N., Deris, M.M., Abawajy, J.H., Arbaiy, N. (eds) Recent Advances in Soft Computing and Data Mining. SCDM 2022. Lecture Notes in Networks and Systems, vol 457. Springer, Cham. https://doi.org/10.1007/978-3-031-00828-3_16

Download citation