alexa Abstract | Neural Network Model in HIV / AIDS Application
ISSN: 1948-1432

Journal of Global Research in Computer Sciences
Open Access

OMICS International organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations

700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)

Research Article Open Access

Abstract

HIV / AIDS is an incurable disease. More than millions of people have HIV positive. However, new medications not only can slow the progression of the infection, but also can suppress the virus, thereby restoring the body’s immune function and permitting many HIVinfected individuals to lead a normal, disease-free life. Many research are going on to predict a better treatment for HIV patients, such as HIV drug prediction, drug resistance testing, predicting side effects for certain regimens etc. The prediction of regimen specification is a challenging research. Since all the patients are unique in their medical history, side effects and allergic in patricular drugs, the physician cannot treat all the patient in the same way. It is common that if a patient with set of certain sympotms consult the physician, the patient may get different opinions regarding the type of the underlying disease. A physician judegement is an important role in this regard. Recent research shows that computational intelligence has been widely used on medical diagonosis to solve complex problem by developing decision support system with the application of Neural Network algorithms. Neural Network is very good area to practice most of the medical problems. It has many algorithms for classification, prediction, image processing etc.A proper utilization of a Neural Network technique to implement a large – scale health services research dataset is one of the most difficult areas in the Neural Network field. It is further complicated due to ill-defined and illstructured factors affecting a functional health status of HIV /AIDS patients. Many of the studies have applied Neural Network technique to classify and predict desired solution or to improve methodological aspects. In this proposed work, we have taken 300 HIV / AIDS patient’s medical history and constructed a model to predict the appropriate regimen specification, which could help the patient to prolong their for maximum years. To construct this model we had been implemented Back Propagation Neural Network algorithm, ART1 Network and Radial Basis Function Network. Back Propagation Neural Network algorithm is used for classification and prediction purpose and also it would work with huge amount of data with large number of iterations. Due to its feed backward nature it could be act as better prediction algorithm. Similary the ART1 Neural Network algorithm has used to classify the patients into two groups active and inactive based on their regimen specification and the Radial Basis Neural Network has also used to prdict the regimen specification. All these three algorithms have used in this work to predict better regimen specification for HIV / AIDS patients.

To read the full article Peer-reviewed Article PDF image

Author(s): M.Lilly Florence and Dr.P.Balasubramanie

Keywords

ANN, BPNN, ART1, RBNN AND HIV / AIDS, Neural Network,Diagnosis of AIDS,Diagnosis of AIDS,Diagnosis of AIDS,Diagnosis of AIDS,Diagnosis of AIDS,Diagnosis of AIDS,Neurological Complications of AIDS,HIV Immunogenetics,AIDS Diagnosis

Share This Page

Additional Info

Loading
Loading Please wait..
 
 
Peer Reviewed Journals
 
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
 
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

 
© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version
adwords