Using different types of neural networks in detection the body's readiness for blood donation and determining the value of each of its parameters using genetic algorithm

Authors

  • Zahra Jafari Islamic Azad University
  • Asma Mahdavi Yousefi Islamic Azad University
  • Saman Rajabi Seraj Higher Education Institute

DOI:

https://doi.org/10.15649/2346075X.998

Keywords:

Perceptron neural network, RBF neural network, blood transfusion, Fisher’s discrimination ratio, genetic algorithm

Abstract

Blood transfusion is of great importance in the medical field, as blood donation centers are responsible for collecting and distributing blood and blood products. Artificial neural network is a data processing system getting ideas from the human brain and designs a data structure that acts like a neuron using programming science and by creating a network between these neurons and combining large amounts of data with smart algorithms and their rapid processing, the network is trained. In this study, data is extracted from the blood transfusion service center and the perceptron neural network, RBF neural network, Fisher’s discrimination ratio and genetic algorithm were examined, and finally the highest possible accuracy from the neural network was achieved.

Author Biographies

Zahra Jafari, Islamic Azad University

Department of Medical Engineering, Faculty of Electrical Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran.

Asma Mahdavi Yousefi, Islamic Azad University

Department of Medical Engineering, Faculty of Electrical Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran

Saman Rajabi, Seraj Higher Education Institute

Assistant Professor of Electrical Engineering Department, Seraj Higher Education Institute, Tabriz, Iran

References

American Red Cross Biomedical Services. Archived from the original on 2009-10-20. Retrieved 2009-10-26.

R. Beale and T. Jackson, Neural Computing (An Introduction), Adam Ililger (1990).
https://doi.org/10.1201/9781420050431

J. Schmidhuber, "Deep Learning in Neural Networks: An Overview". Neural Networks. 61: 85-117. (2015).
https://doi.org/10.1016/j.neunet.2014.09.003

D.Graupe, principles of artificial neural networks, world scientific publishing co. Pte. LTD., Vol.6, second edition, 2007.
https://doi.org/10.1142/6429

U. Orhan, M. Hekim, M. Ozer, "EEG signals classification using the K-means clustering and a multilayer Perceptron neural network model" Expert Systems with Applications 38 (2011) 13475-13481.
https://doi.org/10.1016/j.eswa.2011.04.149

Lowe, D, "Adaptive radial basis function non-linearities and the problem of generalisation", IEE Conf. on Artificial Intelligence and Neural Networks. 1989.

C. K. I. Williams, D. Barber "Bayesian Classification with Gaussian Process" IEEE Transactions on Pattern Analysis and Machine Intelligence (Volume: 20, Issue: 12, Dec 1998).
https://doi.org/10.1109/34.735807

Seryasat, Omid Rahmani, and Javad Haddadnia. "Evaluation of a new ensemble learning framework for mass classification in mammograms." Clinical breast cancer 18.3 (2018): e407-e420.
https://doi.org/10.1016/j.clbc.2017.05.009

J.L.R. Filho, P.C. Treleaven, C. Alippi, Genetic algorithm programming environments, IEEE Comput. 27 (1994) 28-43.
https://doi.org/10.1109/2.294850

Downloads

Published

2020-12-01

How to Cite

Jafari, Z., Mahdavi Yousefi, A., & Rajabi, S. (2020). Using different types of neural networks in detection the body’s readiness for blood donation and determining the value of each of its parameters using genetic algorithm. Innovaciencia, 8(1), 1–10. https://doi.org/10.15649/2346075X.998

Issue

Vol. 8 No. 1 (2020)

Section

Original research and innovation article
Crossref
Scopus
Google Scholar
Europe PMC

Altmetrics

Downloads

Download data is not yet available.

License

All articles published in this scientific journal are protected by copyright. The authors retain copyright and grant the journal the right of first publication, with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0), which permits sharing the work with authorship recognition and without commercial purposes.

Readers may copy and distribute the material from this journal issue for non-commercial purposes in any medium, provided the original work is cited and credit is given to the authors and the journal.

Any commercial use of the material from this journal is strictly prohibited without written permission from the copyright holder.

For more information on the copyright of the journal and open access policies, please visit our website.