Development of Rheumatoid Arthritis Classification from Electronic Image Sensor Using Ensemble Method
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 caus...
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| Format: | Article |
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MDPI AG
2019-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/1/167 |
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| author | Ho Sharon Irraivan Elamvazuthi Cheng-Kai Lu S. Parasuraman Elango Natarajan |
| author_facet | Ho Sharon Irraivan Elamvazuthi Cheng-Kai Lu S. Parasuraman Elango Natarajan |
| author_sort | Ho Sharon |
| collection | DOAJ |
| description | 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’ 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. |
| first_indexed | 2024-04-12T19:40:12Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-12T19:40:12Z |
| publishDate | 2019-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f32995710c1545d19b8de2049cd91d552022-12-22T03:19:06ZengMDPI AGSensors1424-82202019-12-0120116710.3390/s20010167s20010167Development of Rheumatoid Arthritis Classification from Electronic Image Sensor Using Ensemble MethodHo Sharon0Irraivan Elamvazuthi1Cheng-Kai Lu2S. Parasuraman3Elango Natarajan4Smart Assistive and Rehabilitative Technology (SMART) Research Group, Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, MalaysiaSmart Assistive and Rehabilitative Technology (SMART) Research Group, Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, MalaysiaSmart Assistive and Rehabilitative Technology (SMART) Research Group, Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, MalaysiaSchool of Engineering, Monash University Malaysia, 46150 Bandar Sunway, MalaysiaFaculty of Engineering, Technology and Built Environment, UCSI University, 56000 Kuala Lumpur, MalaysiaRheumatoid 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’ 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.https://www.mdpi.com/1424-8220/20/1/167wearable sensorimage sensormachine learningmedical datasetsensemble methodclassification |
| spellingShingle | Ho Sharon Irraivan Elamvazuthi Cheng-Kai Lu S. Parasuraman Elango Natarajan Development of Rheumatoid Arthritis Classification from Electronic Image Sensor Using Ensemble Method Sensors wearable sensor image sensor machine learning medical datasets ensemble method classification |
| title | Development of Rheumatoid Arthritis Classification from Electronic Image Sensor Using Ensemble Method |
| title_full | Development of Rheumatoid Arthritis Classification from Electronic Image Sensor Using Ensemble Method |
| title_fullStr | Development of Rheumatoid Arthritis Classification from Electronic Image Sensor Using Ensemble Method |
| title_full_unstemmed | Development of Rheumatoid Arthritis Classification from Electronic Image Sensor Using Ensemble Method |
| title_short | Development of Rheumatoid Arthritis Classification from Electronic Image Sensor Using Ensemble Method |
| title_sort | development of rheumatoid arthritis classification from electronic image sensor using ensemble method |
| topic | wearable sensor image sensor machine learning medical datasets ensemble method classification |
| url | https://www.mdpi.com/1424-8220/20/1/167 |
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