Skip to main content

Advertisement

SpringerLink
Log in

Innovative feature selection and classification model for heart disease prediction

  • Original Article
  • Published:
Journal of Reliable Intelligent Environments Aims and scope Submit manuscript

Abstract

Heart disease is a complex disease that affects a large number of people worldwide. The timely and accurate detection of heart disease is critical in healthcare, particularly in the field of cardiology. In this article, we proposed a system for diagnosing heart disease that is both efficient and accurate, and it is based on machine-learning techniques. The diagnosis of heart disease is found to be a serious concern, so the diagnosis has to be done remotely and regularly to take the prior action. In the present world, finding the prevalence of heart disease has become a key research area for the researchers and many models have crown proposed in the recent year. The optimization algorithm plays a vital role in heart disease diagnosis with high accuracy. Important goal of this work is to develop a hybrid GCSA which represents a genetic-based crow search algorithm for feature selection and classification using deep convolution neural networks. From the obtained results, the proposed model GCSA shows increase in the classification accuracy by obtaining more than 94% when compared to the other feature selection methods.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Ang JC, Mirzal A, Haron H, Hamed HNA (2015) Supervised, unsupervised, and semi-supervised feature selection: a review on gene selection. IEEE/ACM Trans Comput Biol Bioinform 13(5):971–989

    Article  Google Scholar 

  2. Coronato A, Cuzzocrea A (2020) An innovative risk assessment methodology for medical information systems. IEEE Trans Knowl Data Eng :1–1. https://doi.org/10.1109/tkde.2020.3023553

  3. Ge Z, Song Z, Ding SX, Huang B (2017) Data mining and analytics in the process industry: the role of machine learning. IEEE Access 5:20590–20616

    Article  Google Scholar 

  4. Hira ZM, Gillies DF (2015) A review of feature selection and feature extraction methods applied on microarray data. Adv Bioinform 2015 :1–13. https://doi.org/10.1155/2015/198363

  5. Hu B, Dai Y, Su Y, Moore P, Zhang X, Mao C, Chen J, Xu L (2016) Feature selection for optimized high-dimensional biomedical data using an improved shuffled frog leaping algorithm. IEEE/ACM Trans Comput Biol Bioinform 15(6):1765–1773

    Article  Google Scholar 

  6. Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697

    Article  Google Scholar 

  7. Karunyalakshmi M, Tajunisha N (2017) Classification of cancer datasets using artificial bee colony and deep feed forward neural networks. Int J Adv Res Comput Commun Eng 62:33–41

    Google Scholar 

  8. Manogaran G, Alazab M, Saravanan V, Rawal BS, Shakeel PM, Sundarasekar R, Nagarajan SM, Kadry SN, Montenegro-Marin CE (2020) Machine learning assisted information management scheme in service concentrated IoT. IEEE Trans Ind Inform 17(4):2871–2879

    Article  Google Scholar 

  9. Misra D, Das G, Das D (2020) An IoT based building health monitoring system supported by cloud. J Reliab Intell Environ 6:141–152

    Article  Google Scholar 

  10. Muni Kumar N, Manjula R et al (2014) Role of big data analytics in rural health care—a step towards Svasth Bharath. Int J Comput Sci Inf Technol 5(6):7172–7178

    Google Scholar 

  11. Murugan NS, Devi GU (2018) Detecting spams in social networks using ml algorithms—a review. Int J Environ Waste Manag 21(1):22–36

    Article  Google Scholar 

  12. Murugan NS, Devi GU (2018) Detecting streaming of twitter spam using hybrid method. Wirel Pers Commun 103(2):1353–1374

    Article  Google Scholar 

  13. Murugan NS, Devi GU (2019) Feature extraction using LR-PCA hybridization on twitter data and classification accuracy using machine learning algorithms. Clust Comput 22(6):13965–13974

    Article  Google Scholar 

  14. Nagarajan SM, Deverajan GG, Chatterjee P, Alnumay W, Ghosh U (2021) Effective task scheduling algorithm with deep learning for internet of health things (ioht) in sustainable smart cities. Sustain Cities Soc 71:102945

    Article  Google Scholar 

  15. Nagarajan SM, Muthukumaran V, Murugesan R, Joseph RB, Munirathanam M (2021) Feature selection model for healthcare analysis and classification using classifier ensemble technique. Int J Syst Assur Eng Manag. https://doi.org/10.1007/s13198-021-01126-7

  16. Nagpal A, Gaur D (2015) ModifiedFAST: a new optimal feature subset selection algorithm. J Inf Commun Converg Eng 13(2):113–122

    Google Scholar 

  17. Nalband S, Sundar A, Prince AA, Agarwal A (2016) Feature selection and classification methodology for the detection of knee-joint disorders. Comput Methods Programs Biomed 127:94–104

    Article  Google Scholar 

  18. Ng K, Ghoting A, Steinhubl SR, Stewart WF, Malin B, Sun J (2014) PARAMO: a parallel predictive modeling platform for healthcare analytic research using electronic health records. J Biomed Inform 48:160–170

    Article  Google Scholar 

  19. Paragliola G, Coronato A (2021) An hybrid ECG-based deep network for the early identification of high-risk to major cardiovascular events for hypertension patients. J Biomed Inform 113:103648

    Article  Google Scholar 

  20. Rani AS, Rajalaxmi RR (2015) Unsupervised feature selection using binary bat algorithm. In: 2015 2nd International conference on electronics and communication systems (ICECS). https://doi.org/10.1109/ecs.2015.7124945

  21. Rani P, Kumar R, Ahmed NM, Jain A (2021) A decision support system for heart disease prediction based upon machine learning. J Reliab Intell Environ. https://doi.org/10.1007/s40860-021-00133-6

  22. Saxena K, Sharma R et al (2015) Diabetes mellitus prediction system evaluation using c4. 5 rules and partial tree. In: 2015 4th International conference on reliability, infocom technologies and optimization (ICRITO) (trends and future directions). https://doi.org/10.1109/icrito.2015.7359272

  23. Shahana AH, Preeja V (2016) Survey on feature subset selection for high dimensional data. In: 2016 International conference on circuit, power and computing technologies (ICCPCT). https://doi.org/10.1109/iccpct.2016.7530147

  24. Shardlow M (2016) An analysis of feature selection techniques, vol 1. The University of Manchester, Manchester, pp 1–7

    Google Scholar 

  25. Singh N, Jindal S (2018) Heart disease prediction using classification and feature selection techniques. Int J Adv Res Ideas Innov Technol 4(2). www.IJARIIT.com

  26. Verma L, Srivastava S, Negi P (2016) A hybrid data mining model to predict coronary artery disease cases using non-invasive clinical data. J Med Syst 40(7):178

    Article  Google Scholar 

  27. Xue B, Cervante L, Shang L, Zhang M (2012) A particle swarm optimisation based multi-objective filter approach to feature selection for classification. In: Pacific rim international conference on artificial intelligence. Springer, Berlin, pp 673–685

  28. Zawbaa HM, Emary E, Parv B, Sharawi M (2016) Feature selection approach based on moth-flame optimization algorithm. In: 2016 IEEE Congress on evolutionary computation (CEC). https://doi.org/10.1109/cec.2016.7744378

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India

    Senthil Murugan Nagarajan

  2. Department of Mathematics, School of Advance Sciences, REVA University, Bangalore, India

    V. Muthukumaran & R. Murugesan

  3. Department of Mathematics, Christ Academy Institute for Advance Studies, Bangalore, India

    Rose Bindu Joseph

  4. Department of Mathematics, Rajiv Gandhi University of Knowledge Technologies, R. K. Valley, Nuzividu, Andhra Pradesh, India

    Munirathanam Meram

  5. Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India

    A. Prathik

Authors
  1. Senthil Murugan Nagarajan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. Muthukumaran
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R. Murugesan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rose Bindu Joseph
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Munirathanam Meram
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. A. Prathik
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Senthil Murugan Nagarajan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nagarajan, S.M., Muthukumaran, V., Murugesan, R. et al. Innovative feature selection and classification model for heart disease prediction. J Reliable Intell Environ 8, 333–343 (2022). https://doi.org/10.1007/s40860-021-00152-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40860-021-00152-3

Keywords

Search

Navigation