{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T17:13:55Z","timestamp":1772644435045,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,12,27]],"date-time":"2019-12-27T00:00:00Z","timestamp":1577404800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003093","name":"Ministry of Higher Education, Malaysia","doi-asserted-by":"publisher","award":["0153AB-L28"],"award-info":[{"award-number":["0153AB-L28"]}],"id":[{"id":"10.13039\/501100003093","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Rheumatoid arthritis (RA) is an autoimmune illness that impacts the musculoskeletal system by causing chronic, inflammatory, and systemic effects. The disease often becomes progressive and reduces physical function, causes suffering, fatigue, and articular damage. Over a long period of time, RA causes harm to the bone and cartilage of the joints, weakens the joints\u2019 muscles and tendons, eventually causing joint destruction. Sensors such as accelerometer, wearable sensors, and thermal infrared camera sensor are widely used to gather data for RA. In this paper, the classification of medical disorders based on RA and orthopaedics datasets using Ensemble methods are discussed. The RA dataset was gathered from the analysis of white blood cell classification using features extracted from the image of lymphocytes acquired from a digital microscope with an electronic image sensor. The orthopaedic dataset is a benchmark dataset for this study, as it posed a similar classification problem with several numerical features. Three ensemble algorithms such as bagging, Adaboost, and random subspace were used in the study. These ensemble classifiers use k-NN (K-nearest neighbours) and Random forest (RF) as the base learners of the ensemble classifiers. The data classification is accessed using holdout and 10-fold cross-validation evaluation methods. The assessment was based on set of performance measures such as precision, recall, F-measure, and receiver operating characteristic (ROC) curve. The performance was also measured based on the comparison of the overall classification accuracy rate between different ensembles classifiers and the base learners. Overall, it was found that for Dataset 1, random subspace classifier with k-NN shows the best results in terms of overall accuracy rate of 97.50% and for Dataset 2, bagging-RF shows the highest overall accuracy rate of 94.84% over different ensemble classifiers. The findings indicate that the efficiency of the base classifiers with ensemble classifier have substantially improved.<\/jats:p>","DOI":"10.3390\/s20010167","type":"journal-article","created":{"date-parts":[[2019,12,27]],"date-time":"2019-12-27T11:42:47Z","timestamp":1577446967000},"page":"167","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Development of Rheumatoid Arthritis Classification from Electronic Image Sensor Using Ensemble Method"],"prefix":"10.3390","volume":"20","author":[{"given":"Ho","family":"Sharon","sequence":"first","affiliation":[{"name":"Smart Assistive and Rehabilitative Technology (SMART) Research Group, Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4721-9400","authenticated-orcid":false,"given":"Irraivan","family":"Elamvazuthi","sequence":"additional","affiliation":[{"name":"Smart Assistive and Rehabilitative Technology (SMART) Research Group, Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5819-0754","authenticated-orcid":false,"given":"Cheng-Kai","family":"Lu","sequence":"additional","affiliation":[{"name":"Smart Assistive and Rehabilitative Technology (SMART) Research Group, Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S.","family":"Parasuraman","sequence":"additional","affiliation":[{"name":"School of Engineering, Monash University Malaysia, 46150 Bandar Sunway, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1215-0789","authenticated-orcid":false,"given":"Elango","family":"Natarajan","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Technology and Built Environment, UCSI University, 56000 Kuala Lumpur, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"ITC1-1","DOI":"10.7326\/0003-4819-153-1-201007060-01001","article-title":"Rheumatoid arthritis","volume":"153","author":"Huizinga","year":"2010","journal-title":"Ann. Intern. Med."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Rahim, K.K.A., Elamvazuthi, I., Izhar, L.I., Capi, G., and Rahim, K.N.K.A. (2018). Classification of Human Daily Activities Using Ensemble Methods Based on Smartphone Inertial Sensors. Sensors, 18.","DOI":"10.3390\/s18124132"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Nurhanim, K., Elamvazuthi, I., Izhar, L.I., and Ganesan, T. (2017, January 19\u201321). Classification of Human Activity Based on Smartphone Inertial Sensor Using Support Vector Machine. Proceedings of the IEEE 3rd International Symposium in Robotics and Manufacturing Automation (ROMA), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ROMA.2017.8231736"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1109\/TE.2016.2558163","article-title":"A Brain\u2013Computer Interface Project Applied in Computer Engineering","volume":"59","author":"Katona","year":"2016","journal-title":"IEEE Trans. Educ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1504\/IJHFMS.2011.045000","article-title":"Finger movement measurements in arthritic patients using wearable sensor enabled gloves","volume":"2","author":"Condell","year":"2011","journal-title":"Int. J. Hum. Factors Model. Simul."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Pauk, J., Wasilewska, A., and Ihnatouski, M. (2019). Infrared Thermography Sensor for Disease Activity Detection in Rheumatoid Arthritis Patients. Sensors, 19.","DOI":"10.3390\/s19163444"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"708","DOI":"10.3904\/kjim.2018.349","article-title":"Application of machine learning in rheumatic disease research","volume":"34","author":"Kim","year":"2019","journal-title":"Korean J. Intern. Med."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hasan, K., Islam, S., Samio, M.M.R.K., and Chakrabarty, A. (2018, January 25\u201329). A Machine Learning Approach on Classifying Orthopedic Patients Based on Their Biomechanical Features. Proceedings of the Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and the 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), Kitakyushu, Japan.","DOI":"10.1109\/ICIEV.2018.8641042"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.asoc.2016.01.039","article-title":"Contrast enhancement and brightness preserving of digital mammograms using fuzzy clipped contrast-limited adaptive histogram equalization algorithm","volume":"42","author":"Jenifer","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1166\/jmihi.2014.1246","article-title":"An Efficient Biomedical Imaging Technique for Automatic Detection of Abnormalities in Digital Mammograms","volume":"4","author":"Jenifer","year":"2014","journal-title":"J. Med. Imaging Health Inform."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"51528","DOI":"10.1109\/ACCESS.2018.2869040","article-title":"Enhanced Multi-Objective Teaching-Learning-Based Optimization for Machining of Delrin","volume":"6","author":"Natarajan","year":"2018","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Natarajan, E., Kaviarasan, V., Lim, W.H., Tiang, S.S., Parasuraman, S., and Elango, S. (2019). Non-dominated sorting modified teaching\u2013learning-based optimization for multi-objective machining of polytetrafluoroethylene (PTFE). J. Intell. Manuf., 1\u201325.","DOI":"10.1007\/s10845-019-01486-9"},{"key":"ref_13","unstructured":"Tan, P.N., Michael, S., and Vipin, K. (2016). Introduction to Data Mining, Pearson\/Addison Wesley."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/3-540-45014-9_1","article-title":"Ensemble Methods in Machine Learning","volume":"1857","author":"Dietterich","year":"2000","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_15","unstructured":"Ethem, A. (2009). Introduction to Machine Learning, MIT Press. [2nd ed.]."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kumar, N., and Khatri, S. (2017, January 9\u201310). Implementing WEKA for medical data classification and early disease prediction. Proceedings of the 3rd IEEE International Conference on Computational Intelligence and Communication Technology (CICT), Ghaziabad, India.","DOI":"10.1109\/CIACT.2017.7977277"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5110","DOI":"10.1016\/j.eswa.2009.12.085","article-title":"Evaluation of ensemble methods for diagnosing of valvular heart disease","volume":"37","author":"Das","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Shiezadeh, Z., Sajedi, H., and Aflakie, E. (2015, January 6\u20137). Diagnosis of Rheumatoid Arthritis Using an Ensemble Learning Approach. Proceedings of the Fourth International Conference on Advanced Information Technologies and Applications, Dubai, United Arab Emirates.","DOI":"10.5121\/csit.2015.51512"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chokkalingam, S.P., and Komathy, K. (2013, January 23\u201324). Comparison of different classifier in WEKA for rheumatoid arthritis. Proceedings of the International Conference on Human Computer Interactions (ICHCI), Chennai, India.","DOI":"10.1109\/ICHCI-IEEE.2013.6887821"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/S0950-3579(97)80030-1","article-title":"Review What is early rheumatoid arthritis? definition and diagnosis","volume":"11","author":"Emery","year":"1997","journal-title":"Baillieres Clin. Rheumatol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"814","DOI":"10.1002\/art.1780370606","article-title":"The lag time between onset of symptoms and diagnosis of rheumatoid arthritis","volume":"37","author":"Chan","year":"1994","journal-title":"Arthritis Rheum."},{"key":"ref_22","first-page":"79620M","article-title":"Detection of RA using infrared imaging","volume":"Volume 7962","author":"Frize","year":"2011","journal-title":"Medical Imaging 2011: Image Processing"},{"key":"ref_23","unstructured":"Witten, I.H., and Frank, E. (2005). Data Mining, Practical Machine Learning Tools and Techniques, Elsevier. [2nd ed.]."},{"key":"ref_24","unstructured":"Podgorelec, V., Heri\u010dko, M., and Rozman, I. (2005, January 23\u201324). Improving Mining of Medical Data by Outliers Prediction. Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems (CBMS\u201905), Dublin, Ireland."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lin, C., Karlson, E.W., Canh\u00e3o, H., Miller, T.A., Dligach, D., Chen, P.J., Perez, R.N.G., Shen, Y., Weinblatt, M.E., and Shadick, N.A. (2013). Automatic Prediction of Rheumatoid Arthritis Disease Activity from the Electronic Medical Records. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0069932"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e162","DOI":"10.1136\/amiajnl-2011-000583","article-title":"Portability of an algorithm to identify rheumatoid arthritis in electronic health records","volume":"19","author":"Carroll","year":"2012","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"149","DOI":"10.14445\/22312803\/IJCTT-V25P129","article-title":"Machine Learning Techniques for Automatic Classification of Patients with Fibromyalgia and Arthritis","volume":"25","author":"Rogers","year":"2015","journal-title":"IJCTT"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1016\/j.chest.2018.04.037","article-title":"Big Data and Data Science in Critical Care","volume":"154","author":"Luo","year":"2018","journal-title":"Chest"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning: With Applications in R, Springer.","DOI":"10.1007\/978-1-4614-7138-7"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kuhn, M., and Johnson, K. (2013). Applied Predictive Modeling, Springer.","DOI":"10.1007\/978-1-4614-6849-3"},{"key":"ref_31","unstructured":"(2019, November 12). American College of Rheumatology. Available online: https:\/\/www.rheumatology.org\/Portals\/0\/Files\/1987%20Rheumatoid%20Arthritis%20Classification_Excerpt%201987.pdf."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"R10","DOI":"10.1186\/ar2360","article-title":"3D and thermal surface imaging produces reliable measures of joint shape and temperature: A potential tool for quantifying arthritis","volume":"10","author":"Soalding","year":"2008","journal-title":"Artritis Res. Ther."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s10921-016-0335-y","article-title":"Active IR-Thermal Imaging in Medicine","volume":"35","author":"Kaczmarek","year":"2016","journal-title":"J. Nondestruct. Eval."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1109\/TBME.2015.2463711","article-title":"Towards Quantitative Assessment of RA using Volerteic Ultrasound","volume":"63","author":"Cao","year":"2016","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_35","unstructured":"Eutice, C. (2019, June 15). Rheumatoid Arthritis the Basics. Available online: http:\/\/arthritis.about.com\/od\/rheumatoidarthritis\/p\/rheumatoidfacts.htm."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"066008","DOI":"10.1117\/1.JBO.24.6.066008","article-title":"Detecting inflammation in rheumatoid arthritis using Fourier transform analysis of dorsal optical transmission images from a pilot study","volume":"24","author":"Lighter","year":"2019","journal-title":"J. Biomed. Opt."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1159\/000493277","article-title":"Observational Study of a Wearable Sensor and Smartphone Application Supporting Unsupervised Exercises to Assess Pain and Stiffness","volume":"2","author":"Perraudin","year":"2018","journal-title":"Digit. Biomark."},{"key":"ref_38","first-page":"335","article-title":"A Systematic Approach on Data Pre-processing in Data Mining","volume":"2","author":"Baskar","year":"2013","journal-title":"Compusoft"},{"key":"ref_39","first-page":"1","article-title":"Ensemble Methods in Machine Learning","volume":"Volume 1857","author":"Dietterich","year":"2000","journal-title":"Computer Vision\u2014ECCV 2012"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10462-009-9124-7","article-title":"Ensemble-Based Classifiers","volume":"33","author":"Rokach","year":"2010","journal-title":"Artif. Intell. Rev."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging Predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_42","first-page":"23","article-title":"A desicion-theoretic generalization of on-line learning and an application to boosting","volume":"904","author":"Freund","year":"1995","journal-title":"Model. Data Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/34.709601","article-title":"The random subspace method for constructing decision forests","volume":"20","author":"Ho","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1145\/1656274.1656278","article-title":"The WEKA data mining software: An update","volume":"11","author":"Frank","year":"2009","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1007\/s100440200011","article-title":"Bagging, Boosting and the Random Subspace Method for Linear Classifiers","volume":"5","author":"Skurichina","year":"2002","journal-title":"Pattern Anal. Appl."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhou, Z.H. (2012). Ensemble Methods: Foundations and Algorithms, Chapman & Hall\/CRC.","DOI":"10.1201\/b12207"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2157","DOI":"10.1016\/j.patrec.2007.06.018","article-title":"An experimental evaluation of ensemble methods for EEG signal classification","volume":"28","author":"Sun","year":"2007","journal-title":"Pattern Recognit. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Robnik-\u0160ikonja, M. (2004). Improving Random Forests. European Conference on Machine Learning, Springer.","DOI":"10.1007\/978-3-540-30115-8_34"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1109\/TPDS.2016.2603511","article-title":"A Parallel Random Forest Algorithm for Big Data in a Spark Cloud Computing Environment","volume":"28","author":"Chen","year":"2017","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_50","first-page":"1089","article-title":"No unbiased estimator of the variance of k-fold cross-validation","volume":"5","author":"Bengio","year":"2004","journal-title":"J. Mach. Learn. Res."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Sakr, S., Elshawi, R., Ahmed, A., Qureshi, W.T., Brawner, C., Keteyian, S., Blaha, M.J., and Al-Mallah, M.H. (2018). Using machine learning on cardiorespiratory fitness data for predicting hypertension: The Henry Ford ExercIse Testing (FIT) Project. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0195344"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1016\/j.csda.2009.04.009","article-title":"Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap","volume":"53","author":"Kim","year":"2009","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1895","DOI":"10.1162\/089976698300017197","article-title":"Approximate statistical tests for comparing supervised classification learning algorithms","volume":"10","author":"Dietterich","year":"1998","journal-title":"Neural Comput."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1186\/s12938-015-0014-8","article-title":"Lung segmentation on standard and mobile chest radiographs using oriented Gaussian derivatives filter","volume":"14","author":"Ahmad","year":"2015","journal-title":"Biomed. Eng. Online"},{"key":"ref_55","first-page":"3926","article-title":"Detection of Rheumatoid Arthritis Using Lymphocyte Images","volume":"7","author":"Chokkalingam","year":"2014","journal-title":"J. Adv. Res."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/1\/167\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:46:03Z","timestamp":1760190363000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/1\/167"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,27]]},"references-count":55,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["s20010167"],"URL":"https:\/\/doi.org\/10.3390\/s20010167","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,27]]}}}