Diagnosis of Rheumatoid Arthritis Using an Ensemble Learning Approach

Shiezadeh, Zahra; Sajedi, Hedieh; Aflakie, Elham

doi:10.5121/csit.2015.51512

Cited by 20 publications

(3 citation statements)

References 34 publications

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“…Machine learning methods for early prediction of RA based on electronic health records [25][26][27][28][29], deep learning strategy on X-ray images [30], an ensemble approach for disease gene identification, where EPU achieved an accuracy of 84.8% [31]. The Decision Stump as weak Learner, and Cuckoo search named CS-Boost for early prognosis of the disease [32]. Adaboost based classifier model for early diagnosis of fibromyalgia and arthritis [33], Numerous Neural Network based diagnosis model for arthiritis diagnosis.…”

Section: Related Workmentioning

confidence: 99%

Effective Prediction of Rheumatoid Arthritis (Ra) Diagnosis Using Hybrid Harmony Search with Adaptive Neuro Fuzzy Inference System

Shanmugam¹,

Saravanabhavan²,

Arunkumar³

2021

Preprint

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One of the severe auto immune diseases that affects the entire human body is Rheumatoid Arthritis (RA), the disease triggers one’s immune system to attack the inner linings of bones and causes severe inflammation of the synovium. The continuous erosion of bone lining leads to permanent loss of the joint, accounting this severity the early prognosis of the disease is a significant and inevitable process. But, the sign and symptoms of the disease are always uncertain. The symptom of RA disease is similar to other inflammatory diseases, so highly experienced experts can identify the disease in its early stage. To support the clinicians and technicians for early prognosis of the disease, a computer-aided decision support model based on Harmony Search –Adaptive Neuro Fuzzy Inference System is presented in this study. The Harmony search algorithm is employed to select the optimal features, and ANFIS is adopted to perform classification. To demonstrate the effectiveness of the model, metrics such as Accuracy, Sensitivity, Specificity, Precision, Recall, F-measure, Positive Predictive Value, Negative Predictive Value, Root Mean Square Error, and Mean Absolute Error are employed and evaluated in MATLAB simulation environment. The proposed HS-ANFIS outperformed other models developed in this research and existing works of literature.

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Section: Related Workmentioning

confidence: 99%

Effective Prediction of Rheumatoid Arthritis (Ra) Diagnosis Using Hybrid Harmony Search with Adaptive Neuro Fuzzy Inference System

Shanmugam¹,

Saravanabhavan²,

Arunkumar³

2021

Preprint

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“…It can be concluded that with base classifier MLP, boosting method has a better performance compared to a single classifier, whereby the sensitivity of 97.3% and specificity of 94% were obtained. In the work by Shiezadeh, et al [18], algorithm such as ADAboost with J48 learner was chosen as the base learner, decision tree and CSboost were applied to the dataset of 18 attributes. It is proven in his research that CSboost is able to provide the highest accuracy among the classification algorithms.…”

Section: Related Workmentioning

confidence: 99%

Development of Rheumatoid Arthritis Classification from Electronic Image Sensor Using Ensemble Method

Sharon

Elamvazuthi

et al. 2019

Sensors

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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.

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“…Cuckoo search algorithm is used by this. Singh et al [8] used a Fuzzy Logic Controller (FLC) to design an Arthitis diagnosis system. Zadeh's fuzzy set theory is applied in this algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Critical Review on Medical Image Processing Techniques

2020

jcr

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Medical image processing is highly a vital task in the field of health informatics. Various techniques are used to process the images captured using methods like Medical Resonance Imaging (MRI), Computed Tomography(CT), Positron Emission Tomography (PET) and the process includes, denoising, feature extraction, segmentation and classification. Different methods are implemented for every process. Process of eliminating noise from an acquired image is termed as denoising and various filters are designed for the same. Different features like local binary pattern, gray level similarity are extracted for segmentation process. Clustering algorithms like fuzzy-c-means, fuzzy-k-means algorithms are used for image segmentation. Particle Swarm Optimization (PSO), convolutional neural networks are also used for the purpose of image segmentation. Performance of these methods are compared using various performance evaluation metrics like, precision, recall, accuracy, sensitivity.

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Diagnosis of Rheumatoid Arthritis Using an Ensemble Learning Approach

Abstract: Rheumatoid arthritis is one of the diseases that its cause is unknown yet

Cited by 20 publications

References 34 publications

Effective Prediction of Rheumatoid Arthritis (Ra) Diagnosis Using Hybrid Harmony Search with Adaptive Neuro Fuzzy Inference System

Effective Prediction of Rheumatoid Arthritis (Ra) Diagnosis Using Hybrid Harmony Search with Adaptive Neuro Fuzzy Inference System

Development of Rheumatoid Arthritis Classification from Electronic Image Sensor Using Ensemble Method

A Critical Review on Medical Image Processing Techniques

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