Open Access Open Access  Restricted Access Subscription or Fee Access

Experiment and Results Evaluation of Medical Diagnostic System Developed Through Artificial Feed Forward Neural Networks Using Optimal Back Propagation Algorithm

Ashish Dehariya, Neetesh Gupta, Dr. Bhupendra Verma

Abstract


In its previous part, contribution reviews over the application of artificial neural networks to medical diagnosis and characterizes its advantages and problems in the context of the medical background..Then paradigm of neural networks is introduced and the main problems of medical data base and the basic approaches for training and testing a network by medical data are described. Additionally, the problem of interfacing the network and its result is given and the optimal Back propagation algorithm approach with its flow diagram is presented. On one hand growing neural network and on the other rules based systems works for Medical diagnosis purposes. Diagnostic capabilities of artificial neural network using optimal back propagation based medical diagnosis system is less error prone than fuzzified system of medical diagnosis so it is more advantageous than fuzzification based approach. Proposed diagnosis system has the capabilities to take intelligent decision based on given symptoms of diseases takes as a input and outcomes shows the bacterial infection level so this concept can be used for the purpose of developing Efficient medical diagnosis system and definitely will help to overcome the problems of lack of Experts in Medical fields, wrong diagnosis decisions due to human experts stress also it would help to enhance healthy population amount if such a system will reach to common peoples and use by them.


Keywords


Artificial Feed Forward Neural Networks, Back Propagation, Fuzzification, Medical Diagnosis, Optimal Back Propagation

Full Text:

PDF

References


S. Moein, S. A. Monadjemi, & P. Moallem (2008) “A Novel Fuzzy-Neural Based Medical Diagnosis System”, World Academy of Science, Engineering and Technology 37 ,2008

A Pomi,F Olivera,BMC Medical Infromatics and Decision making. context sensitive auto associative memories as expert system in Medical Diagnosis,BioMed Central,2006

Nadia Nedijah,Ajit Abraham,Luiza M.Mourelle.”Hybrid artificial neural network”. International journel of Neural network computing and application.DOI,10.1007/s00521-007 0083-0©Springer-Verlag London Limited 2007.

Ashish Dehariya, Vijay K. Chaudhari, Ilyas Khan, Saurabh Karsoliya.”An Effective Approach for Medical Diagnosis preceded by Artificial Neural Network Ensemble”.Paper is registered for IEEE Publication Title: ICECT 2011 3rd International Conference on Electronics Computer Technology(ICECT 2011). ISBN:978-1-4244-8677-9.

Fuster, V., Alexander, R. W., & O’Rourke, R. A. (2001). Hurst’s the heart. USA: McGraw Hill Professional.

Lou, X., and Dimitrakopoulos, R., 2003, Data-driven fuzzy analysis in quantitative mineral assessment: Computers and Geosciences,v. 29, p. 3-13.

Tsoukalas, L.H., and Uhrig, R.E., 1997, Fuzzy and neural approaches in engineering: New York, John Wiley and Sons, Inc., 587p.

R.Schalkof, Artificial Neural Networks, Mc Graw Hill ,1994.

Ashish Dehariya,Vijay K.Chaudhari,IlyasKhan,Saurabh Karsoliya.” A Novel flow for Reasoning of Medical Diagnostic System using Artificial Feed Forward Neural Networks” .International journal of computer science and Engineering(IJCSE).ISSN : 0975-3397

L A Zadeh,Biological application of the theory of fuzzy sets and System,Biocybermatics of the Central nervous System, 1969.

H. Kordylewski., D. Graupe, Kai Liu, “ A novel large memory neural network as an aid in medical diagnosis applications” IEEE Transactions on Information Technology in Biomedicine , Sept. 2001 V 5#3.


Refbacks

  • There are currently no refbacks.