Frost Denoised Regressive Feature Extraction based Relevance Vector Classification for Rheumatoid Arthritis Disease Prediction
Keywords:
Rheumatoidarthritis,chronicinflammatory,imagedenoising,improvisedfrost,denoising filter, kernelized relevance vector classification.Abstract
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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D. Uma Nandhini, Janani Naga Sai Pravallika, Beulah Jabaseeli and S. Udhayakumar, “Prediction of Rheumatoid Arthritis disease using improved segmentation algorithm”, Materials Today Proceedings, Elsevier, Volume 62, Part 7, 2022, Pages 4940-4945. https://doi.org/10.1016/j.matpr.2022.03.684
Shanmugam Sundaramurthy and Preethi Jayavel, “A hybrid Grey Wolf Optimization and Particle Swarm Optimization with C4.5 approach for prediction of Rheumatoid Arthritis”, Applied Soft Computing, Elsevier, Volume 94, September 2020, Pages 1-15. https://doi.org/10.1016/j.asoc.2020.106500
Michael Mahler, Laura Martinez-Prat, Jeffrey A. Sparks and Kevin D. Deane, “Precision medicine in the care of rheumatoid arthritis: Focus on prediction and prevention of future clinically-apparent disease”, Autoimmunity Reviews, Elsevier, Volume 19, Issue 5, May 2020, Pages 1-15. https://doi.org/10.1016/j.autrev.2020.102506
Khalid Ahmad Omer Aldabbagh and Dashty Abbas Al-Bustany, “Relationship of serum copper and HLADR4 tissue typing to disease activity and severity in patients with rheumatoid arthritis: A cross sectional study”, Annals of Medicine and Surgery, Elsevier, Volume 73, January 2022, Pages 1-15. doi: 10.1016/j.amsu.2021.103193
Hong Ki Min, Hae-Rim Kim, Sang-Heon Lee, Kwi Young KangcSung-HwanParkdSeung-KiKwok “Time-averaged DAS28 and HAQ predict cardiovascular disease in patients with rheumatoid arthritis: Data from KORONA registry”,Joint Bone Spine, Elsevier, Volume 89, Issue 5, October 2022, Pages 1-14. DOI: 10.1016/j.jbspin.2022.105401
Henry W. Zheng, Veena K. Ranganath, Lucas C. Perry, David A. Chetrit, Karla M. Criner, Angela Q. Pham, Richard Seto, Sitaram Vangala, David A. Elashoff and Alex A. T. Bui, “Evaluation of an automated phenotyping algorithm for rheumatoid arthritis”, Journal of Biomedical Informatics, Elsevier, Volume 135, November 2022, Pages 1-12. doi: https://doi.org/10.1016/j.jbi.2022.104214.
Zhanping Zhou, Chenyang Zhao, Hui Qiao, Ming Wang, Yuchen Guo, Qian Wang, Rui Zhang, Huaiyu Wu, Fajin Dong, Zhenhong Qi, Jianchu Li, Xinping Tian, Xiaofeng Zeng, Yuxin Jiang, Feng Xu, Qionghai Dai and Meng Yang, “RATING: Medical knowledge-guided rheumatoid arthritis assessment from multimodal ultrasound images via deep learning”, Patterns, Elsevier, Volume 3, Issue 10, 14 October 2022, Pages 1-15. DOI: 10.1016/j.patter.2022.100592
Xinqiang Song, Yu Zhang, Erqin Dai, Lei Wang and Hongtao Du, “Prediction of triptolide targets in rheumatoid arthritis using network pharmacology and molecular docking”,International Immunopharmacology, Elsevier, Volume 80, March 2020, Pages 1-12. https://doi.org/10.1016/j.intimp.2019.106179
B. Chithra and R. Nedunchezhian, “Dynamic neutrosophic cognitive map with improved cuckoo search algorithm (DNCM-ICSA) and ensemble classifier for rheumatoid arthritis (RA) disease”,Journal of King Saud University - Computer and Information Sciences, Elsevier, Volume 34, Issue 6, Part B, June 2022, Pages 3236-3246. https://doi.org/10.1016/j.jksuci.2020.06.011
Janick Rohrbach, Tobias Reinhard, Beate Sick and Oliver Dürr, “Bone erosion scoring for rheumatoid arthritis with deep convolutional neural networks”,Computers& Electrical Engineering, Elsevier, Volume 78, September 2019, Pages 472-481. https://doi.org/10.1016/j.compeleceng.2019.08.003
Matsuo, H., Kamada, M.,Imamura, A. etal. Machine learning-based prediction ofrelapse in rheumatoid arthritis patients using data on ultrasound examination and blood test. Sci Rep 12, 7224(2022).https://doi.org/10.1038/s41598-022-11361-y
Deane KD, HolersVM.Rheumatoid Arthritis Pathogenesis, Prediction,and Prevention: An Emerging Paradigm Shift.ArthritisRheumatol. 2021Feb;73(2):181-19. doi:https://doi.org/10.1002/art.41417
Yu M, Cong H, Chen W Fang M. Feature extraction of rotor- stator rubbing faults based on harmonic fusion vector bispectrum, ITD and Hjorth parameters. Proceedings of the Institution of Mechanical Engineers,Part G: Journal of Aerospace Engineering.2023;237(3):600-613.doi: https://doi.org/10.1177/09544100221107256
Sulthana, A.R., Jaithunbi,A.K. Varying combination of feature extraction and modified support vector machines based prediction of my ocardial infarction. Evolving Systems 13, 777–794 (2022).https://doi.org/10.1007/s12530-021-09410-4
Bhandari Nikita, WalambeRahee, Kotecha Ketan, Khare Satyajeet P. (2022). A comprehensive survey on computational learning methods for analysis of gene expression data. Frontiers in Molecular Biosciences.9.https://doi.org/10.3389/fmolb.2022.907150
Momtazmanesh S, Nowroozi A, Rezaei N. (2022). Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review.Rheumatology and Therapy.9(5):1249-1304.https://doi.org/10.1007/s40744-022-00475-4.
Kalweit M, Walker UA, Finckh A, Müller R, Kalweit G, Scherer A, Boedecker J, Hügle T. (2021). Personalized prediction of disease activity in patients with rheumatoid arthritis using an adaptive deep neural network.PLoS One. 2021.16(6):e0252289.https://doi.org/10.1371/journal.pone.0252289
Norgeot B, GlicksbergBS,Trupin L, et al. (2019).Assessment of a Deep Learning Model Based on Electronic Health Record Data to Forecast Clinical Outcomes in PatientsWith Rheumatoid Arthritis.JAMA Network Open. 2019;2(3):e190606.https://doi.org/10.1001/jamanetworkopen.2019.0606
Neha Chaudhary, Thi Nguyet Que Nguyen, Muddassar Ahmad, Robert Harrington, Caroline A. Jefferies, Grainne Kearns, Aidan D. Meade and Claire Wynne, “A spectroscopic diagnostic for rheumatoid arthritis using liquid biopsies”,Clinical Spectroscopy, Elsevier, Volume 3, December 2021, Pages 1-18. https://doi.org/10.1016/j.clispe.2021.100009
C. A. Wijbrandts and P. P. Tak, “Prediction of Response to Targeted Treatment in Rheumatoid Arthritis”, Mayo Clinic Proceedings, Elsevier, Volume 92, Issue 7, July 2017, Pages 1129-1143. https://doi.org/10.1016/j.mayocp.2017.05.009
Luciano Walenty Xavier Cejnog, Teofilode Campos, Valeria Meirelles Carril Elui, Roberto Marcondes Cesar, “A framework for automatic hand range of motion evaluation of rheumatoid arthritis patients”, Informatics in Medicine Unlocked, Elsevier, Volume 23, 2021, Pages 1-12 DOI:10.1016/j.imu.2021.100544
Tengfei Yang, Haijiang Zhu, Xiaoyu Gao, Yiyuan Zhang, Yanan Hui and Fangfang Wang, “Grading of Metacarpophalangeal Rheumatoid Arthritis on Ultrasound Images Using Machine Learning Algorithms”, IEEE Access, Volume 8, March 2020, Pages 67137 – 67146. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9043487
Kristine L. Richardson, Caitlin N. Teague, Samer Mabrouk, Brandi N. Nevius, Goktug C. Ozmen, Rachel S. Graham, Daniel P. Zachs, Adam Tuma, Erik J. Peterson, Hubert H. Lim and Omer T. Inan, “Quantifying Rheumatoid Arthritis Disease Activity Using a Multimodal Sensing Knee Brace”, IEEE Transactions on Biomedical Engineering, Volume 69, Issue 12, December 2022, Pages 3772 – 3783. https://doi.org/10.1109/TBME.2022.3177074
Kunlin Cao, David M. Mills, Ralf G. Thiele and Kedar A. Patwardhan, “Toward Quantitative Assessment of Rheumatoid Arthritis Using Volumetric Ultrasound”, IEEE Transactions on Biomedical Engineering, Volume 63, Issue 2, February 2016, Pages 449 – 458
Yinghe Huo, Koen L. Vincken, Desiree van der Heijde, Maria J. H. De Hair, Floris P. Lafeber and Max A. Viergever, “Automatic Quantification of Radiographic Finger Joint Space Width of Patients with Early Rheumatoid Arthritis”, IEEE Transactions on Biomedical Engineering, Volume 63, Issue 10, October 2016, Pages 2177 – 2186DOI: https://doi.org/10.1109/TBME.2015.2512941
A. Schwaighofe, V. Tresp, P. Mayer, A. Krause, J. Beuthan, H. Rost, G. Metzger, G.A. Muller and A.K. Scheel, “Classification of rheumatoid joint inflammation based on laser imaging”, IEEE Transactions on Biomedical Engineering, Volume 50, Issue 3, March 2003, Pages 375 – 382DOI: 10.1109/TBME.2003.808827
Michelle Baker, Sarah Denman-Johnson, Bindi S. Brook, Ian Gaywood and Markus R. Owen, “Mathematical modeling of cytokine-mediated inflammation in rheumatoid arthritis”, Mathematical Medicine and Biology: A Journal of the IMA, Volume 30, Issue 4, December 2013, Pages 311 – 337. https://doi.org/10.1093/imammb/dqs026
U. Hass, H. Brodin, A. Andersson and J. Persson, “Assistive technology selection: a study of participation of users with rheumatoid arthritis”, IEEE Transactions on Rehabilitation Engineering, Volume 5, Issue 3, Pages 263 – 275DOI: 10.1109/86.623018
Referred links:
https://www.sciencedirect.com/search?qs=rheumatoid%20arthritis%20classification
https://www.kaggle.com/datasets/tommyngx/kneeoa
https://www.sciencedirect.com/science/article/pii/S0306456522002182
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