eprintid: 11258
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/12/58
datestamp: 2023-11-10 03:25:46
lastmod: 2023-11-10 03:25:46
status_changed: 2023-11-10 01:14:50
type: conference_item
metadata_visibility: show
creators_name: Sharon, H.
creators_name: Elamvazuthi, I.
creators_name: Lu, C.K.
creators_name: Parasuraman, S.
creators_name: Natarajan, E.
title: Classification of Rheumatoid Arthritis using Machine Learning Algorithms
ispublished: pub
keywords: Adaptive boosting; Classification (of information); Database systems; Decision trees; Diseases; Infrared imaging; Learning systems; Soft computing; Support vector machines, Assessment criteria; Base classifiers; Classification accuracy; Decision process; Ensemble algorithms; Machine learning techniques; Rheumatoid arthritis; Temperature values, Machine learning
note: cited By 7; Conference of 17th IEEE Student Conference on Research and Development, SCOReD 2019 ; Conference Date: 15 October 2019 Through 17 October 2019; Conference Code:154444
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, ensemble algorithms such as bagging, AdaBoost and random subspace with base classifier such as random forest and SVM were trained and tested using the assessment criteria such as accuracy, precision, sensitivity and AUC using Weka tool. From the preliminary finding of this paper, it can be concluded that with base classifier SVM, bagging has better classification accuracy over the others and with base classifier random forest Adaboost slightly outperformed other models for rheumatoid arthritis dataset. © 2019 IEEE.
date: 2019
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075630467&doi=10.1109%2fSCORED.2019.8896344&partnerID=40&md5=a1d4d81de35dfa70328d19cd42160dcb
id_number: 10.1109/SCORED.2019.8896344
full_text_status: none
publication: 2019 IEEE Student Conference on Research and Development, SCOReD 2019
pagerange: 345-350
refereed: TRUE
isbn: 9781728126135
citation:   Sharon, H. and Elamvazuthi, I. and Lu, C.K. and Parasuraman, S. and Natarajan, E.  (2019) Classification of Rheumatoid Arthritis using Machine Learning Algorithms.  In: UNSPECIFIED.