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Reseach Article

Application of Perceptron Networks in Recommending Medical Diagnosis

by Venkata Karthik Gullapalli, Rahul Brungi, Gopichand G
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 113 - Number 4
Year of Publication: 2015
Authors: Venkata Karthik Gullapalli, Rahul Brungi, Gopichand G
10.5120/19811-1609

Venkata Karthik Gullapalli, Rahul Brungi, Gopichand G . Application of Perceptron Networks in Recommending Medical Diagnosis. International Journal of Computer Applications. 113, 4 ( March 2015), 1-5. DOI=10.5120/19811-1609

@article{ 10.5120/19811-1609,
author = { Venkata Karthik Gullapalli, Rahul Brungi, Gopichand G },
title = { Application of Perceptron Networks in Recommending Medical Diagnosis },
journal = { International Journal of Computer Applications },
issue_date = { March 2015 },
volume = { 113 },
number = { 4 },
month = { March },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume113/number4/19811-1609/ },
doi = { 10.5120/19811-1609 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:50:03.896537+05:30
%A Venkata Karthik Gullapalli
%A Rahul Brungi
%A Gopichand G
%T Application of Perceptron Networks in Recommending Medical Diagnosis
%J International Journal of Computer Applications
%@ 0975-8887
%V 113
%N 4
%P 1-5
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Artificial intelligence applications in medicine is the major and evolutionary topic in the technology world. Neural networks is an important branch of machine learning which is inspired from biological neural networks. Neural networks are useful in making proper decisions in rational environments with uncertainty. The neural networks perform better computation with high power with the help of the multiple interconnected neurons which act as processing elements. Decision theory along with probabilistic theory gives the good way to make the right decisions. Neural Network systems help in linking the health observations with the health knowledge database to take better decisions for good health. The ability of a neural network to learn by example can be implemented for taking decisions that would increase the rate of providing the better medical care facilities. This paper presents a novel methodology to implement supervised learning networks that is perceptron networks in medical diagnosis for providing such good decisions to the doctors in helping patients by also providing a good health care.

References
  1. Moja, L; Kwag, KH; Lytras, T; Bertizzolo, L; Brandt, L; Pecoraro, V; Rigon, G; Vaona, A; Ruggiero, F; Mangia, M; Iorio, A; Kunnamo, I; Bonovas, S (December 2014). "Effectiveness of computerized decision support systems linked to electronic health records: a systematic review and meta-analysis. " American journal of public health 104 (12): e12–22.
  2. Wagholikar, K; Kathy L. MacLaughlin; Thomas M Kastner; Petra M Casey; Michael Henry; Robert A Greenes; Hongfang Liu; Rajeev Chaudhry (2013). "Formative evaluation of the accuracy of a clinical decision support system for cervical cancer screening". Journal of American Medical Informatics Association. pp. 747–759. Retrieved 6 March 2013.
  3. Rothman, Brian; Joan. C. Leonard; Michael. M. Vigoda (2012). "Future of electronic health records: implications for decision support". Mount Sinai Journal of Medicine 79 (6): 757–768.
  4. Venkata Karthik Gullapalli, Rahul Brungi, "A Novel Methodology to Implement Optimization Algorithms in Machine Learning", International Journal of Computer Applications (0975 – 8887), Volume 112 – No 4, February 2015.
  5. Loya, S. R. ; Kawamoto, K; Chatwin, C; Huser, V (2014). "Service oriented architecture for clinical decision support: A systematic review and future directions". Journal of Medical Systems.
  6. Yael Zenziper, Daniel Kurnik, Noa Markovits, Amitai Ziv, Ari Shamiss, Hillel Halkin and Ronen Loebstein, "Implementation of a Clinical Decision Support System for Computerized Drug Prescription Entries in a Large Tertiary Care Hospital", IMAJ, volume 16, May 2014.
  7. Niazkhani Z, Pirnejad H, Berg M, Aarts J. The impact of computerized provider order entry systems on inpatient clinical workflow: a literature review. J Am Med . Inform Assoc 2009; 16: 539-49.
  8. Chaffee BW, Zimmerman CR. Developing and implementing clinical decision support for use in a computerized prescriber-order-entry system. Am J Health Syst Pharm. 2010 Mar 1; 67 (5):391-400.
  9. Bell LM, Grundmeier R, Localio R, Zorc J, Fiks AG, Zhang X, Stephens TB, Swietlik M, Guevara JP. Electronic health record-based decision support to improve asthma care: A cluster-randomized trial. Pediatrics. 2010 Apr; 125(4):e770-7.
  10. Venkata Karthik Gullapalli and Aishwarya Asesh, Data Trawling and Security Strategies, ISSN – 2278-8727, IOSR Journal of Computer Engineering, Volume 16, Issue 6, Ver. 1, Nov - Dec 2014.
  11. D Tadaki, "Advanced Surgical Imaging. " Biomedical Science & Engineering Conference, 2009, IEEE, pages 1 -1.
  12. M. Frize, C. M. Ennett, M. Stevenson, H. Trigg. "Clinical decision support system for intensive care units: using artificial neural networks. " Medical Engineering & Physics, 2001, pages 217-225.
Index Terms

Computer Science
Information Sciences

Keywords

Clinical Decision Support System Perceptron Processing Elements Weights Activation Functions healthcare system