Skip to main content

Early Stage Detection of Cardiac Related Diseases by Using Artificial Neural Network

  • Conference paper
  • First Online:
Recent Advances in Soft Computing and Data Mining (SCDM 2022)

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

Included in the following conference series:

  • 301 Accesses

Abstract

In this study, a method is proposed in which accurate prediction at early stage is important of cardiac patient for efficiently treating. The novelty of this research is using feature extraction with the help of some techniques of signals processing. Ten different kinds of sensors are used are metal oxide semiconductors that are used for sensing the different gasses that are emanating from the human body. Moreover, ECG, SPO2 and oxygen sensors are used for further processing. Various experiments are performed that identify 5, 10, 15 and 20 subjects every subject is identified and scanned as 1000 different features. The signals that are received are analogue and with the help of Arduino they convert to digital signals. An architecture is trained on the dataset that is developed. Sensitivity, f-measures, accuracy and specificity are the standards that are used for the evaluation of the model that is proposed as identification of human odour. The accuracy for this model is more than 85%.

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

Access this chapter

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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. LAST: Foundations, Promises and Uncertainties of Personalized Medicine Address Correspondence to: Medicine, Baltimore, pp. 15–21 (2007). https://doi.org/10.1002/msj.20005

  2. Mozaffarian, D., et al.: Executive summary: heart disease and stroke statistics-2015 update: a report from the American heart association. Circulation 131(4), 434–441 (2015). https://doi.org/10.1161/CIR.0000000000000157

    Article  Google Scholar 

  3. Lloyd-Jones, D.M.: Cardiovascular risk prediction: basic concepts, current status, and future directions. Circulation 121(15), 1768–1777 (2010). https://doi.org/10.1161/CIRCULATIONAHA.109.849166

    Article  Google Scholar 

  4. Eilat-Adar, S., Sinai, T., Yosefy, C., Henkin, Y.: Nutritional recommendations for cardiovascular disease prevention. Nutrients 5(9), 3646–3683 (2013)

    Article  Google Scholar 

  5. Lupton, D., Maslen, S.: Telemedicine and the senses: a review. Sociol. Heal. Illn. 39(8), 1557–1571 (2017). https://doi.org/10.1111/1467-9566.12617

    Article  Google Scholar 

  6. Ben-Zeev, D.: Mobile health for all: public-private partnerships can create a new mental health landscape. JMIR Ment. Heal. 3(2), e26 (2016). https://doi.org/10.2196/mental.5843

    Article  Google Scholar 

  7. Ali, F., et al.: A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion. Inf. Fusion 63, 208–222 (2020). https://doi.org/10.1016/j.inffus.2020.06.008

    Article  Google Scholar 

  8. Gilanie, G., Ijaz, U.: Computer aided diagnosis of brain abnormalities using texture analysis of MRI images. (January), 1–12 (2019). https://doi.org/10.1002/ima.22312

  9. Ribeiro, A.H., et al.: Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat. Commun. 11(1), 1–9 (2020). https://doi.org/10.1038/s41467-020-15432-4

    Article  Google Scholar 

  10. Deperlioglu, O., Kose, U., Gupta, D., Khanna, A., Sangaiah, A.K.: Diagnosis of heart diseases by a secure internet of health things system based on autoencoder deep neural network. Comput. Commun. 162(April), 31–50 (2020). https://doi.org/10.1016/j.comcom.2020.08.011

    Article  Google Scholar 

  11. Krithiga, B., Sabari, P., Jayasri, I., Anjali, I.: Early detection of coronary heart disease by using naive Bayes algorithm. J. Phys. Conf. Ser. 1717, 012040 (2021). https://doi.org/10.1088/1742-6596/1717/1/012040

    Article  Google Scholar 

  12. Akkaş, M.A., Sokullu, R., Ertürk Çetin, H.: Healthcare and patient monitoring using IoT. Internet Things 11(2020), 100173 (2020). https://doi.org/10.1016/j.iot.2020.100173

    Article  Google Scholar 

  13. Bajwa, U.I., Shah, A.A., Anwar, M.W., Gilanie, G., Ejaz Bajwa, A.: Computer-aided detection (CADe) system for detection of malignant lung nodules in CT slices - a key for early lung cancer detection. Curr. Med. Imaging Rev. 14(3), 422–429 (2018). https://doi.org/10.2174/1573405613666170614083951

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Erum Wazir .

Editor information

Editors and Affiliations

Copyright information

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

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wazir, E., Gilanie, G., Rehman, N., Ullah, H., Mushtaq, M.F. (2022). Early Stage Detection of Cardiac Related Diseases by Using Artificial Neural Network. 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_36

Download citation

Publish with us

Policies and ethics