Skip to main content
SpringerLink
Log in

Heart Disease Prediction Using Machine Learning Techniques: A Quantitative Review

  • Conference paper
  • First Online:
International Conference on Innovative Computing and Communications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1394))

Abstract

Heart diseases or the cardiovascular diseases are the main reasons for a large number of deaths in the world today. Heart disease affects the functioning of blood vessels and can cause coronary artery infections that in turn lead to weakening of patient’s body. Therefore, there is a need for reliable, accurate, and feasible system that can diagnose the heart disease on time so that the cardiac patient is given an efficient treatment before it leads to a severe complication, finally resulting in a heart attack. During the last many years, machine learning (ML) algorithms and techniques have been applied to various available heart disease datasets for automatic prediction, diagnosis, and treatment of the heart disease. This paper presents a thorough survey of various machine learning techniques and analyzes their performances which are used for efficient prediction, diagnosis, and treatment of various heart diseases. Some machine learning techniques used for the prediction of the occurrence of heart diseases that are surveyed in the proposed paper are support vector machine (SVM), decision tree (DT), Naïve Bayes (NB), K-nearest neighbor (KNN), artificial neural network (ANN), etc. Then, the average prediction accuracy was calculated for each technique to find out overall best and worst performing technique. According to the results, the highest average prediction accuracy was achieved by ANN (86.91%), whereas C4.5 decision tree technique came up with the lowest average prediction accuracy of 74.0%.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. H. Ahmed, E.M.G. Younis, A. Hendawi, A.A. Ali, Heart disease identification from patients’ social posts, machine learning solution on Spark. Futur. Gener. Comput. Syst. 111, 714–722 (2020). https://doi.org/10.1016/j.future.2019.09.056

    Article  Google Scholar 

  2. Y. Hao, M. Usama, J. Yang, M.S. Hossain, A. Ghoneim, Recurrent convolutional neural network based multimodal disease risk prediction. Futur. Gener. Comput. Syst. 92, 76–83 (2019). https://doi.org/10.1016/j.future.2018.09.031

    Article  Google Scholar 

  3. M. Ashraf et al., Prediction of cardiovascular disease through cutting-edge deep learning technologies: an empirical study based on TENSORFLOW, PYTORCH and KERAS. Adv. Intell. Syst. Comput. 1165, 239–255 (2021). https://doi.org/10.1007/978-981-15-5113-0_18

    Article  Google Scholar 

  4. R. Mohd, M.A. Butt, M.Z. Baba, GWLM–NARX: Grey Wolf Levenberg–Marquardt-based neural network for rainfall prediction. Data Technol. Appl. 54(1), 85–102 (2020). https://doi.org/10.1108/DTA-08-2019-0130

    Article  Google Scholar 

  5. R. Mohd, M.A. Butt, M. Zaman Baba, SALM-NARX: self adaptive LM-based NARX model for the prediction of rainfall, in Proceedings of International Conference on I-SMAC (IoT Social Mobile, Analytics and Cloud), I-SMAC 2018, pp. 580–585, 2019. https://doi.org/10.1109/I-SMAC.2018.8653747

  6. M. Ashraf, M. Zaman, M. Ahmed, An intelligent prediction system for educational data mining based on ensemble and filtering approaches. Proc. Comput. Sci. 167(2019), 1471–1483 (2020). https://doi.org/10.1016/j.procs.2020.03.358

    Article  Google Scholar 

  7. M. Ashraf, M. Zaman, M. Ahmed, To Ameliorate Classification Accuracy Using Ensemble Vote Approach and Base Classifiers, vol. 813 (Springer Singapore, 2019)

    Google Scholar 

  8. M. Ashraf, M. Zaman, M. Ahmed, Performance analysis and different subject combinations: an empirical and analytical discourse of educational data mining, in Proceedings of the 8th International Conference Confluence 2018 on Cloud Computing, Data Science and Engineering, Confluence 2018, Aug 2018, pp. 287–292. https://doi.org/10.1109/CONFLUENCE.2018.8442633

  9. M. Ashraf, M. Zaman, M. Ahmed, Using ensemble StackingC method and base classifiers to ameliorate prediction accuracy of pedagogical data. Proc. Comput. Sci. 132, 1021–1040 (2018). https://doi.org/10.1016/j.procs.2018.05.018

  10. M. Zaman, S. Kaul, M. Ahmed, Analytical comparison between the information gain and Gini index using historical geographical data. Int. J. Adv. Comput. Sci. Appl. 11(5), 429–440 (2020). https://doi.org/10.14569/IJACSA.2020.0110557

    Article  Google Scholar 

  11. N.M. Mir, S. Khan, M.A. Butt, M. Zaman, An experimental evaluation of Bayesian classifiers applied to intrusion detection. Indian J. Sci. Technol. 9(12) (2016). https://doi.org/10.17485/ijst/2016/v9i12/86291

  12. M. Zaman, S.M.K. Quadri, M.A. Butt, Information translation: a practitioners approach. Lect. Notes Eng. Comput. Sci. 1, 45–47 (2012)

    Google Scholar 

  13. V.V. Ramalingam, A. Dandapath, M. Karthik Raja, Heart disease prediction using machine learning techniques: a survey. Int. J. Eng. Technol. 7(2.8), 684–687 (2018). https://doi.org/10.14419/ijet.v7i2.8.10557

  14. M. Chala Beyene, Survey on prediction and analysis the occurrence of heart disease using data mining techniques 118(8), 165–174 (2018) [Online]. Available http://www.ijpam.eu

  15. https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47

  16. M. Jabbar, P. Chandra, B. Deekshatulu, Cluster based association rule mining for heart attack prediction. J. Theor. Appl. Inf. Technol. 32(2), 196–201 (2011)

    Google Scholar 

  17. R. Chitra, Heart attack prediction system using fuzzy C means classifier. IOSR J. Comput. Eng. 14(2), 23–31 (2013). https://doi.org/10.9790/0661-1422331

    Article  Google Scholar 

  18. C.B. Gokulnath, S.P. Shantharajah, An optimized feature selection based on genetic approach and support vector machine for heart disease. Cluster Comput. 22(s6), 14777–14787 (2019). https://doi.org/10.1007/s10586-018-2416-4

    Article  Google Scholar 

  19. M.S. Amin, Y.K. Chiam, K.D. Varathan, Identification of significant features and data mining techniques in predicting heart disease. Telemat. Inform. 36, 82–93 (2019). https://doi.org/10.1016/j.tele.2018.11.007

  20. S. Prakash, K. Sangeetha, N. Ramkumar, An optimal criterion feature selection method for prediction and effective analysis of heart disease. Cluster Comput. 22(s5), 11957–11963 (2019). https://doi.org/10.1007/s10586-017-1530-z

    Article  Google Scholar 

  21. S. Bashir, Z.S. Khan, F. Hassan Khan, A. Anjum, K. Bashir, Improving heart disease prediction using feature selection approaches, in Proceedings of 2019 16th International Bhurban Conference on Application Science Technology IBCAST 2019, pp. 619–623, 2019. https://doi.org/10.1109/IBCAST.2019.8667106

  22. P. Mamatha Alex, S.P. Shaji, Prediction and diagnosis of heart disease patients using data mining technique, in Proceedings of 2019 IEEE International Conference on Communication Signal Processing ICCSP 2019, pp. 848–852, 2019. https://doi.org/10.1109/ICCSP.2019.8697977

  23. E.E. Tripoliti et al., HEARTEN KMS—a knowledge management system targeting the management of patients with heart failure. J. Biomed. Inform. 94, 103203 (2019). https://doi.org/10.1016/j.jbi.2019.103203

  24. S. Mohan, C. Thirumalai, G. Srivastava, Effective heart disease prediction using hybrid machine learning techniques. IEEE Access 7, 81542–81554 (2019). https://doi.org/10.1109/ACCESS.2019.2923707

    Article  Google Scholar 

  25. K. Chandra Shekar, P. Chandra, K. Venugopala Rao, An Ensemble Classifier Characterized by Genetic Algorithm with Decision Tree for the Prophecy of Heart Disease, vol. 74 (Springer Singapore, 2019)

    Google Scholar 

  26. M. Tarawneh, O. Embarak, Hybrid Approach for Heart Disease Prediction Using Data Mining Techniques, vol. 29 (Springer International Publishing, 2019)

    Google Scholar 

  27. L. Ali, A. Rahman, A. Khan, M. Zhou, A. Javeed, J.A. Khan, An automated diagnostic system for heart disease prediction based on χ2 statistical model and optimally configured deep neural network. IEEE Access 7, 34938–34945 (2019). https://doi.org/10.1109/ACCESS.2019.2904800

    Article  Google Scholar 

  28. C.B.C. Latha, S.C. Jeeva, Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques. Inform. Med. Unlocked 16, 100203 (2019). https://doi.org/10.1016/j.imu.2019.100203

  29. N.B. Muppalaneni, M. Ma, S. Gurumoorthy, Soft Computing and Medical Bioinformatics (Springer Singapore, 2019)

    Google Scholar 

  30. F.S. Alotaibi, Implementation of machine learning model to predict heart failure disease. Int. J. Adv. Comput. Sci. Appl. 10(6), 261–268 (2019). https://doi.org/10.14569/ijacsa.2019.0100637

    Article  Google Scholar 

  31. K. Raza, Improving the Prediction Accuracy of Heart Disease With Ensemble Learning and Majority Voting Rule (Elsevier Inc., 2019)

    Google Scholar 

  32. K.M.Z. Hasan, S. Datta, M.Z. Hasan, N. Zahan, Automated prediction of heart disease patients using sparse discriminant analysis, in 2nd International Conference on Electrical Computing Communication Engineering ECCE 2019, pp. 7–9, 2019.https://doi.org/10.1109/ECACE.2019.8679279

  33. R. Singh, E. Rajesh, Prediction of heart disease by clustering and classification techniques. Int. J. Comput. Sci. Eng. 7(5), 861–866 (2019). https://doi.org/10.26438/ijcse/v7i5.861866

    Article  Google Scholar 

  34. S.D. Desai, S. Giraddi, P. Narayankar, N.R. Pudakalakatti, S. Sulegaon, Back-Propagation Neural Network Versus Logistic Regression in Heart Disease Classification, vol. 702 (Springer Singapore, 2019)

    Google Scholar 

  35. S. Thaiparnit, S. Kritsanasung, N. Chumuang, A classification for patients with heart disease based on hoeffding tree, in JCSSE 2019—16th International Joint Conference on Computing Science Software Engineering Knowledge Evolution Toward Singular Man-Machine Intelligent, pp. 352–357, 2019. https://doi.org/10.1109/JCSSE.2019.8864158

  36. I.K.A. Enriko,“Comparative study of heart disease diagnosis using top ten data mining classification algorithms. ACM Int. Conf. Proceeding Ser. 159–164 (2019). https://doi.org/10.1145/3338188.3338220

  37. M. Akgül, Ö.E. Sönmez, T. Özcan, Diagnosis of heart disease using an intelligent method: a hybrid ANN—GA approach. Adv. Intell. Syst. Comput. 1029, 1250–1257 (2020). https://doi.org/10.1007/978-3-030-23756-1_147

    Article  Google Scholar 

  38. D. Mehanović, Z. Mašetić, D. Kečo, Prediction of heart diseases using majority voting ensemble method. IFMBE Proc. 73, 491–498 (2020). https://doi.org/10.1007/978-3-030-17971-7_73

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Sciences, University of Kashmir, Srinagar, India

    Lubna Riyaz & Muheet Ahmed Butt

  2. Directorate of IT & SS, University of Kashmir, Srinagar, India

    Majid Zaman

  3. Department of Food Technology, Jamia Hamdard, New Delhi, India

    Omeera Ayob

Authors
  1. Lubna Riyaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Muheet Ahmed Butt
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Majid Zaman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Omeera Ayob
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Maharaja Agrasen Institute of Technology, Delhi, India

    Ashish Khanna

  2. Department of Computer Science Engineering, Maharaja Agrasen Institute of Technology, Rohini, Delhi, India

    Deepak Gupta

  3. Rajnagar Mahavidyalaya, Birbhum, India

    Siddhartha Bhattacharyya

  4. Computer and Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  5. Department of Computer Science, Shaheed Sukhdev College of Business Studies, Rohini, Delhi, India

    Sameer Anand

  6. Department of Computer Science, Shaheed Sukhdev College of Business Studies, Rohini, Delhi, India

    Ajay Jaiswal

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Riyaz, L., Butt, M.A., Zaman, M., Ayob, O. (2022). Heart Disease Prediction Using Machine Learning Techniques: A Quantitative Review. In: Khanna, A., Gupta, D., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1394. Springer, Singapore. https://doi.org/10.1007/978-981-16-3071-2_8

Download citation

Publish with us

Policies and ethics

Search

Navigation