Skip to main content
Book cover

International Conference on Intelligent Computing & Optimization

ICO 2020: Intelligent Computing and Optimization pp 1097–1110Cite as

An Efficient Machine Learning-Based Decision-Level Fusion Model to Predict Cardiovascular Disease

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1324)

Abstract

The world’s primary cause of mortality is cardiovascular disease at present. Identifying the risk early could reduce the rate of death. Sometimes, it is difficult for a person to undergo an expensive test regularly. So, there should be a system that can predict the presence of cardiovascular disease by analyzing the basic symptoms. Researchers have focused on building machine learning-based prediction systems to make the process more simple and efficient and reduce both doctors’ and patients’ burdens. In this paper, a decision level fusion model is designed to predict cardiovascular disease with the help of machine learning algorithms that are multilayer neural network and the K Nearest Neighbor (KNN). The decision of each model was merged for the final decision to improves the accuracy. Here Cleveland dataset was used for ANN and KNN, which contains the information of 303 patients with eight attributes. In this two-class classification, ANN gave 92.10% accuracy, and KNN gave 88.16%. After fusing the decision of them, we got an accuracy of 93.42% that performed much better than two of them. The result was obtained by using 75% data in training.

Keywords

  • Cardiovascular disease
  • Machine learning
  • Artificial neural network
  • knn
  • Decision level fusion

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-68154-8_92
  • Chapter length: 14 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   129.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-68154-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   169.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.

References

  1. Christensen, T., Frandsen, A., Glazier, S., Humpherys, J., Kartchner, D.: Machine learning methods for disease prediction with claims data. In: 2018 IEEE International Conference on Healthcare Informatics (ICHI), pp. 467–4674. IEEE (2018)

    Google Scholar 

  2. Dua, D., Graff, C.: UCI machine learning repository (2017). http://archive.ics.uci.edu/ml

  3. Harimoorthy, K., Thangavelu, M.: Multi-disease prediction model using improved svm-radial bias technique in healthcare monitoring system. J. Ambient Intell. Hum. Comput. 1–9 (2020)

    Google Scholar 

  4. Hasan, T.T., Jasim, M.H., Hashim, I.A.: Heart disease diagnosis system based on multi-layer perceptron neural network and support vector machine. Int. J. Curr. Eng. Technol. 77(55), 2277–4106 (2017)

    Google Scholar 

  5. Karayılan, T., Kılıç, Ö.: Prediction of heart disease using neural network. In: 2017 International Conference on Computer Science and Engineering (UBMK), pp. 719–723. IEEE (2017)

    Google Scholar 

  6. Kotsiantis, S., Kanellopoulos, D., Pintelas, P.: Data preprocessing for supervised leaning. Int. J. Comput. Sci. 1(2), 111–117 (2006)

    Google Scholar 

  7. Louridi, N., Amar, M., El Ouahidi, B.: Identification of cardiovascular diseases using machine learning. In: 2019 7th Mediterranean Congress of Telecommunications (CMT), pp. 1–6. IEEE (2019)

    Google Scholar 

  8. Matin, A., Mahmud, F., Ahmed, T., Ejaz, M.S.: Weighted score level fusion of iris and face to identify an individual. In: 2017 International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 1–4. IEEE (2017)

    Google Scholar 

  9. Mohan, S., Thirumalai, C., Srivastava, G.: Effective heart disease prediction using hybrid machine learning techniques. IEEE Access 7, 81542–81554 (2019)

    CrossRef  Google Scholar 

  10. Nourmohammadi-Khiarak, J., Feizi-Derakhshi, M.R., Behrouzi, K., Mazaheri, S., Zamani-Harghalani, Y., Tayebi, R.M.: New hybrid method for heart disease diagnosis utilizing optimization algorithm in feature selection. Health Tech. 1–12 (2019)

    Google Scholar 

  11. Ramya Perumal, K.A.: Early prediction of coronary heart disease from cleveland dataset using machine learning techniques. Int. J. Adv. Sci. Technol. 29(06), 4225 – 4234, May 2020. http://sersc.org/journals/index.php/IJAST/article/view/16428

  12. Krishnan, S., Geetha, J.S.: Prediction of heart disease using machine learning algorithms. In: 1st International Conference on Innovations in Information and Communication Technology (ICIICT). IEEE (2019)

    Google Scholar 

  13. Shah, D., Patel, S., Bharti, S.K.: Heart disease prediction using machine learning techniques. SN Comput. Sci. 1(6), 1–6 (2020)

    CrossRef  Google Scholar 

  14. Telnoni, P.A., Budiawan, R., Qana’a, M.: Comparison of machine learning classification method on text-based case in twitter. In: 2019 International Conference on ICT for Smart Society (ICISS). vol. 7, pp. 1–5. IEEE (2019)

    Google Scholar 

  15. Thomas, J., Princy, R.T.: Human heart disease prediction system using data mining techniques. In: 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT), pp. 1–5. IEEE (2016)

    Google Scholar 

  16. Vasant, P., Zelinka, I., Weber, G.W.: Intelligent Computing & Optimization, vol. 866. Springer, Berlin (2018)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Rajshahi University of Engineering and Technology, Rajshahi, 6204, Bangladesh

    Hafsa Binte Kibria  & Abdul Matin

Authors
  1. Hafsa Binte Kibria
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdul Matin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hafsa Binte Kibria .

Editor information

Editors and Affiliations

  1. Department of Fundamental and Applied Sciences, Universiti Teknologi Petronas, Tronoh, Perak, Malaysia

    Dr. Pandian Vasant

  2. Faculty of Electrical Engineering and Computer Science, VŠB TU Ostrava, Ostrava, Czech Republic

    Prof. Ivan Zelinka

  3. Faculty of Engineering Management, Poznan University of Technology, Poznan, Poland

    Prof. Gerhard-Wilhelm Weber

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Kibria , H.B., Matin, A. (2021). An Efficient Machine Learning-Based Decision-Level Fusion Model to Predict Cardiovascular Disease. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham. https://doi.org/10.1007/978-3-030-68154-8_92

Download citation