Survey On Heart Disease Detection Using Deep Neural with Django Framework

Authors

  • Anjali Sanjay Kumar  Department of Computer Engineering, Zeal College of Engineering and Research, Pune, Maharashtra, India
  • Dr. Swapnaja A. Ubale  Department of Computer Engineering, Zeal College of Engineering and Research, Pune, Maharashtra, India

Keywords:

UCI dataset, Training data, Testing data, DNN

Abstract

A cardiovascular breakdown dataset with only numerical properties should be converted to image data for evaluation using DNN's potential expansions. Coronary load is a measure of how unhealthy the heart is. With cardio vascular devastation, the term cardiovascular difficulties are frequently used. Coronary stock channels deliver blood to the heart, and limiting coronary partners is the beast justification for cardiovascular collapse. Cardiovascular contamination is regarded as one of the most important topics in the field of data analysis. The coronary course issue is the major justification for respiratory dissatisfaction in the United States. Males have more cardiovascular restlessness than females. According to a WHO audit, 24 % in India had rejected the holder due to a heart problem. Experts have identified the several components that increase the risk of cardiovascular disease and coronary vein disease pollution.

References

  1. Dengqing Zhang,1,2 Yunyi Chen,3 Yuxuan Chen,3 Shengyi Ye,1,2 Wenyu Cai,1,2 Junxue Jiang,1,2 Yechuan Xu,1,2 Gongfeng Zheng,1,2 and Ming Chen1,4 “Heart Disease Prediction Based on the Embedded Feature Selection Method and Deep Neural Network“ Received21 Aug 2021 Revised07 Sep 2021 Accepted16 Sep 2021 Published29 Sep 2021
  2. P Kalpana1 , S Shiyam Vignesh1 , L M P Surya1 , V Vishnu Prasad “Prediction of Heart Disease Using Machine Learning “Journal of Physics: Conference Series 1916 (2021) 012022
  3. Awais Mehmood1 • Munwar Iqbal1 • Zahid Mehmood2 • Aun Irtaza1 • Marriam Nawaz1 • Tahira Nazir1 • Momina Masood1 “Prediction of Heart Disease Using Deep Convolutional Neural Networks “Received: 6 May 2020 / Accepted: 2 November 2020 © King Fahd University of Petroleum & Minerals 2021
  4. S. Verma and A. Gupta, "Effective Prediction of Heart Disease Using Data Mining and Machine Learning: A Review," 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), 2021, pp. 249-253, doi: 10.1109/ICAIS50930.2021.9395963.
  5. U. J. khan, A. oberoi and J. Gill, "Hybrid Classification for Heart Disease Prediction using Artificial Intelligence," 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), 2021, pp. 1779-1785, doi: 10.1109/ICCMC51019.2021.9418345.
  6. Syed Nawaz Pasha1, Dadi Ramesh2, Sallauddin Mohmmad3, A. Harshavardhan2 and Shabana “Cardiovascular disease prediction using deep learning techniques “OP Conference Series: Materials Science and Engineering, Volume 981, International Conference on Recent Advancements in Engineering and Management (ICRAEM-2020) 9-10 October 2020, Warangal, India Citation Syed Nawaz Pasha et al 2020 IOP Conf. Ser.: Mater. Sci. Eng. 981 022006
  7. Harshit Jindal1, Sarthak Agrawal1, Rishabh Khera1, Rachna Jain2 and Preeti Nagrath2 “Heart disease prediction using machine learning algorithms“ IOP Conference Series: Materials Science and Engineering, Volume 1022, 1st International Conference on Computational Research and Data Analytics (ICCRDA 2020) 24th October 2020, Rajpura, India
  8. M. T. Islam, S. R. Rafa and M. G. Kibria, "Early Prediction of Heart Disease Using PCA and Hybrid Genetic Algorithm with k-Means," 2020 23rd International Conference on Computer and Information Technology (ICCIT), 2020, pp. 1-6, doi: 10.1109/ICCIT51783.2020.9392655.
  9. Mohd Ashraf, M. A. Rizvi and Himanshu Sharma “Improved Heart Disease Prediction Using Deep Neural Network“Volume 8 No.2 April-June 2019 pp 49-54
  10. S. Bashir, Z. S. Khan, F. Hassan Khan, A. Anjum and K. Bashir, "Improving Heart Disease Prediction Using Feature Selection Approaches," 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST), 2019, pp. 619-623, doi: 10.1109/IBCAST.2019.8667106.
  11. R. Latha and P. Vetrivelan, "Blood Viscosity based Heart Disease Risk Prediction Model in Edge/Fog Computing," 2019 11th International Conference on Communication Systems & Networks (COMSNETS), 2019, pp. 833-837, doi: 10.1109/COMSNETS.2019.8711358.
  12. Ramalingam, Dandapath, Ayantan Raja, M 2018/03/19 684 Heart disease prediction using machine learning techniques: A survey, VL - 7 DOI - 10.14419/ijet.v7i2.8.10557 International Journal of Engineering & Technology.
  13. A. Chauhan, A. Jain, P. Sharma and V. Deep, "Heart Disease Prediction using Evolutionary Rule Learning," 2018 4th International Conference on Computational Intelligence & Communication Technology (CICT), 2018, pp. 1-4, doi: 10.1109/CIACT.2018.8480271.
  14. A. S. Ladkat, S. S. Patankar and J. V. Kulkarni, "Modified matched filter kernel for classification of hard exudate," 2016 International Conference on Inventive Computation Technologies (ICICT), 2016, pp. 1-6, doi: 10.1109/INVENTIVE.2016.7830123.
  15. A. S. Ladkat, A. A. Date and S. S. Inamdar, "Development and comparison of serial and parallel image processing algorithms," 2016 International Conference on Inventive Computation Technologies (ICICT), 2016, pp. 1-4, doi: 10.1109/INVENTIVE.2016.7824894.

International Journal of Scientific Research in Science, Engineering and Technology

Downloads

Published

2022-05-07

Issue

Volume 9, Issue 3, May-June-2022

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

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

How to Cite

[1]
Anjali Sanjay Kumar, Dr. Swapnaja A. Ubale, " Survey On Heart Disease Detection Using Deep Neural with Django Framework, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 9, Issue 3, pp.586-592, May-June-2022.