eprintid: 9506
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/95/06
datestamp: 2023-11-09 16:36:09
lastmod: 2023-11-09 16:36:09
status_changed: 2023-11-09 16:29:09
type: conference_item
metadata_visibility: show
creators_name: Ho, S.
creators_name: Elamvazuthi, I.
creators_name: Lu, C.K.
title: Classification of Rheumatoid Arthritis using Machine Learning Algorithms
ispublished: pub
keywords: Adaptive boosting; Classification (of information); Database systems; Diseases; Infrared imaging; Learning systems; Manufacture; Robotics; Soft computing, Ada boost classifiers; Assessment criteria; Classification accuracy; Decision process; Machine learning techniques; Rheumatoid arthritis; Temperature values; Weka tool, Machine learning
note: cited By 9; Conference of 4th IEEE International Symposium in Robotics and Manufacturing Automation, ROMA 2018 ; Conference Date: 10 December 2018 Through 12 December 2018; Conference Code:157694
abstract: Rheumatoid Arthritis (RA) is a persistent provocative ailment that effects and decimates the joints of wrist, finger, and feet. If left untreated, one can lose their ability to lead a normal life. RA is the most typical fiery joint inflammation, influencing around 1-2 of the total populace. Throughout the years, soft computing played an important part in helping ailment analysis in doctor's decision process. The main aim of this study is to investigate the possibility of applying machine learning techniques to the analysis of RA characteristics. As a preliminary work, a credible database has been identified to be used for this research. The database has outputs of array temperature values from thermal imaging for the joints of hand. Furthermore, this database which consists of 8 attributes and 32 instances, are used to determine the performance in terms of accuracy for the classification of different algorithms. In this preliminary work, algorithms such as bagging, J48 and AdaBoost classifiers were trained and tested using the assessment criteria such as accuracy, specificity and sensitivity using Weka tool. From the preliminary finding of this paper, it can be concluded that bagging has better classification accuracy over the others for rheumatoid arthritis dataset. © 2018 IEEE.
date: 2018
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080022874&doi=10.1109%2fROMA46407.2018.8986700&partnerID=40&md5=36b9c07c40ded99f1adc963f1d49056e
id_number: 10.1109/ROMA46407.2018.8986700
full_text_status: none
publication: 2018 IEEE 4th International Symposium in Robotics and Manufacturing Automation, ROMA 2018
refereed: TRUE
isbn: 9781728103747
citation:   Ho, S. and Elamvazuthi, I. and Lu, C.K.  (2018) Classification of Rheumatoid Arthritis using Machine Learning Algorithms.  In: UNSPECIFIED.