By Topic

Data Mining Applied to Diagnose Diseases Caused by Lymphotropic Virus: a Performance Analysis

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

6 Author(s)

This paper proposes a new methodology to diagnose the rheumatology manifestations and HTLV-I-Associated Myelopathy/Tropical Spastic Paraparesis, or HAM/TSP, in patients who have Lymphotropic virus of T cells in Humans or HTLV of type I and II. Computational intelligence algorithms are used to classify HTLV patient carriers with or without the presence of rheumatology manifestations and of HAM / TSP. A benchmarking is performed among artificial neural intelligence, naïve bayes, Bayesian networks and decision tree to evaluate the most suitable technique for solving this application issue. The obtained results demonstrate the potential of the methodology on the helping non-specialist doctors to classify the patient with the disease suspicion.

Published in:

Latin America Transactions, IEEE (Revista IEEE America Latina)  (Volume:10 ,  Issue: 1 )