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

Abstract

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.

Molecular Imaging of Inflammatory Disease

“…The traditional machine learning techniques can be characterized based on the extracted features and the type of classifier. The feature extraction techniques include gray-level co-occurrence matrix (GLCM), gray-level run-length (GLRL), speededup robust feature extraction (SURF), and dual-tree m-band wavelet transform (DTMBWT) algorithms [171][172][173][174][175][176]. The classifiers include support vector machine (SVM), k-nearest neighbor (K-NN), Random Forest (ensemble classifiers on three ensemble algorithms: bagging, Adaboost, and random subspace), and fuzzy c-means clustering (FCM) [177].…”

The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN

“…The number of neighbours and the types of distance metrics are the main factors in k-NN architecture. The k-NN is widely used in pattern recognition because of its strong generalisation and simple implementation (Sharon et al, 2019). However, EEG's high dimensionality usually hampers k-NN efficiency.…”

“…However, EEG's high dimensionality usually hampers k-NN efficiency. The complexity of these characteristic spaces increases exponentially with the number of features (Sharon et al, 2019). In such a case, an approach that may leverage the advantages of k-NN classifier without being adversely affected by the sparsity of high-dimensional data would be highly favoured, and the well-known ensemble learning technique effectively takes advantage of high-dimensionality (Ho, 1998a).…”

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

“…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].…”