Frost Denoised Regressive Feature Extraction based Relevance Vector Classification for Rheumatoid Arthritis Disease Prediction


  • G.Hemamalini, V.Maniraj


Rheumatoidarthritis,chronicinflammatory,imagedenoising,improvisedfrost,denoising filter, kernelized relevance vector classification.


Objective:Theproposed method aims to predict Rheumatoid Arthritis (RA) disease using Deep Learning Techniques.

Methods: A new technique called Frost Denoised Regressive Feature Extraction-based Relevance Vector Classification (FDRFE-RVC) has been introduced. The FDRFE-RVC technique performs three key processes: image denoising, feature extraction, and classification. It first utilizes an Improved Frost Denoising Filter to enhance image quality and minimize the mean square error. Then, it applies the Michael Index DeFries-Fulker (MIDF) Regression to extract shape, color, and texture features.

Finding: It uses Kernelized Relevance Vector Classification with the extracted features to predict the disease with greater efficiency. An experimental assessment of the FDRFE-RVC technique reveals a significant improvement in accuracy levels, achieving 95% accuracy and a reduced time of 42 milliseconds compared to the existing Novel Gaussian filtering and segmentation algorithm and HGWO-C4.5 methods.

Novelty:The FDRFE-RVC technique offers a promising solution for RA diagnosis with improved accuracy,enhanced image quality,and faster diagnosis times. The proposed FDRFE-RVC technique reduces disease prediction time by 36% and 24%, reduces the false positive rate by 6%, and increases disease prediction accuracy by 11% compared to the existing method.


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How to Cite

G.Hemamalini. (2024). Frost Denoised Regressive Feature Extraction based Relevance Vector Classification for Rheumatoid Arthritis Disease Prediction. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 2426–2437. Retrieved from



Research Article