IoT Based Heart Attack Early Prediction

Authors

  • Shweta Gajbhiye  Assistant Professor, Department of Computer Science and Engineering, Priyadarshini J. L. College of Engineering, Nagpur, Maharashtra, India
  • Bharati Vyas  BE, Department of Computer Science and Engineering, Priyadarshini J. L. College of Engineering, Nagpur, Maharashtra, India
  • Shrushti Shrikhande  BE, Department of Computer Science and Engineering, Priyadarshini J. L. College of Engineering, Nagpur, Maharashtra, India
  • Anshika Janbandhu  BE, Department of Computer Science and Engineering, Priyadarshini J. L. College of Engineering, Nagpur, Maharashtra, India
  • Komal Nagpure  BE, Department of Computer Science and Engineering, Priyadarshini J. L. College of Engineering, Nagpur, Maharashtra, India
  • Mrunali Agashe  BE, Department of Computer Science and Engineering, Priyadarshini J. L. College of Engineering, Nagpur, Maharashtra, India

Keywords:

Heart Attack, IoT (Internet Of Things), Health Parameters, Heart Disease Prediction, Oximeter, Heart rate sensor, Android App.

Abstract

Heart attack is most common disease that engulfs the patient’s precious life. This disease attacks a man so in a flash that it barely gets treated. Many systems that detect heart attack came into the picture but they do have many flaws. Some system does not work properly in cold weather and other doesn’t give accuracy. To have a solution for Heart attack utilization of IoT and sensor system could be beneficial. Heart Attack Early Prediction System predicts the Heart rate and accordingly sends the notification to the patient’s doctor and acquaintances.

References

  1. Milan Kumari, Sunila Godara, Comparative Study of Data Mining Classification Methods in Cardiovascular Disease Prediction, IJCST Vol. 2, Issue 2, June 2011.
  2. Niti Guru, Anil Dahiya, Navin Rajpal, Decision Support System for Heart Disease Diagnosis Using Neural Network, Delhi Business Review, Vol. 8, No. 1, January-June 2007.
  3. Sellappan Palaniappan, Rafiah Awang, Intelligent Heart Disease Prediction System Using Data Mining Technique, 978-1-4244- 1968-5/08/25.00 2008 IEEE.
  4. http://www.nlm.nih.gov/medlineplus/magazine/issues/winter11 
  5. Bourouis, A., Feham, M., and Bouchachia, A.(2011), Ubiquitous Mobile Health Monitoring System for Elderly (UMHMSE), International Journal of Computer Science and Information Technology, Vol.2, No. 3, June, pp. 74-82
  6. Yuce, M. R.(2010) Implementation of wireless body area networks for healthcare systems, Sensor and Actuators A:Physical, Vol. 162, No. 1, July, pp. 116-129
  7. Lei Clifton, David A. Clifton, Marco A. F. Pimentel, Peter J. Watkinson, and Lionel Tarassenko (2014), Predictive Monitoring of Mobile Patients by Combining Clinical Observations with Data From Wearable Sensors, IEEE Journal of Biomedical and Health Informatics, Vol. 18, No. 3, May , pp. 722-730
  8. Parane, K.A., Patil, N.C. ; Poojara, S.R. ; Kamble, T.S(2014) Cloud based Intelligent Healthcare Monitoring System, In the proceedings of International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), February 7-8, Ghaziabad, Indian, pp. 697-701

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2019-02-28

Issue

Volume 5, Issue 1, January-February-2019

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Shweta Gajbhiye, Bharati Vyas, Shrushti Shrikhande, Anshika Janbandhu, Komal Nagpure, Mrunali Agashe, " IoT Based Heart Attack Early Prediction" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 1, pp.448-451, January-February-2019.