Enhancing Rheumatoid Arthritis Detection using Bio-Inspired Feature Selection of GOA Model with DBN Classification

Rheumatoid arthritis (RA) is a debilitating autoimmune disease where early and precise diagnosis is crucial for effective intervention. This study introduces an advanced GOAT Optimisation Algorithm (GOA) for feature selection combined with a Deep Belief Network (DBN) to significantly improve RA dete...

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Published in:2025 International Conference on Intelligent Computing and Knowledge Extraction (ICICKE) pp. 1 - 8
Main Authors: L K, Jayashree, Kumar. J, Santosh
Format: Conference Proceeding
Language:English
Published: IEEE 06.06.2025
Subjects:
Accuracy
Arthritis
autoimmune disease diagnosis
Biological system modeling
biomarker selection
Biomarkers
Classification algorithms
deep belief network
Feature extraction
GOAT optimisation algorithm (GOA)
intelligent healthcare
Medical diagnostic imaging
Optimization
rheumatoid arthritis
Sensitivity
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Abstract Rheumatoid arthritis (RA) is a debilitating autoimmune disease where early and precise diagnosis is crucial for effective intervention. This study introduces an advanced GOAT Optimisation Algorithm (GOA) for feature selection combined with a Deep Belief Network (DBN) to significantly improve RA detection accuracy. Unlike conventional methods, GOA incorporates adaptive mechanisms for enhanced exploration-exploitation balance, enabling more efficient selection of discriminative biomarkers from high-dimensional clinical data. The optimised features are then classified using a DBN with hierarchical feature learning capabilities. Evaluated on a comprehensive RA dataset, our GOAT-DBN framework achieves 98.7% accuracy, 98.2% sensitivity, and 99.1% specificity, surpassing existing approaches like PSO-ANN (96.7%) and standard GOA-DBN (98.2%). Notably, GOAT reduces feature dimensionality by 72% while improving diagnostic reliability, demonstrating its superiority in handling complex medical data patterns. The proposed system not only provides a clinically viable tool for early RA diagnosis but also establishes GOAT as a superior bio-inspired optimiser for medical feature selection problems.
AbstractList Rheumatoid arthritis (RA) is a debilitating autoimmune disease where early and precise diagnosis is crucial for effective intervention. This study introduces an advanced GOAT Optimisation Algorithm (GOA) for feature selection combined with a Deep Belief Network (DBN) to significantly improve RA detection accuracy. Unlike conventional methods, GOA incorporates adaptive mechanisms for enhanced exploration-exploitation balance, enabling more efficient selection of discriminative biomarkers from high-dimensional clinical data. The optimised features are then classified using a DBN with hierarchical feature learning capabilities. Evaluated on a comprehensive RA dataset, our GOAT-DBN framework achieves 98.7% accuracy, 98.2% sensitivity, and 99.1% specificity, surpassing existing approaches like PSO-ANN (96.7%) and standard GOA-DBN (98.2%). Notably, GOAT reduces feature dimensionality by 72% while improving diagnostic reliability, demonstrating its superiority in handling complex medical data patterns. The proposed system not only provides a clinically viable tool for early RA diagnosis but also establishes GOAT as a superior bio-inspired optimiser for medical feature selection problems.
Author Kumar. J, Santosh
L K, Jayashree
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  givenname: Santosh
  surname: Kumar. J
  fullname: Kumar. J, Santosh
  email: santoshkumar-ct@dsu.edu.in
  organization: Dayananda Sagar University,Department of Computer Science and Technology,Bengaluru,India
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Snippet Rheumatoid arthritis (RA) is a debilitating autoimmune disease where early and precise diagnosis is crucial for effective intervention. This study introduces...
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SubjectTerms Accuracy
Arthritis
autoimmune disease diagnosis
Biological system modeling
biomarker selection
Biomarkers
Classification algorithms
deep belief network
Feature extraction
GOAT optimisation algorithm (GOA)
intelligent healthcare
Medical diagnostic imaging
Optimization
rheumatoid arthritis
Sensitivity
Title Enhancing Rheumatoid Arthritis Detection using Bio-Inspired Feature Selection of GOA Model with DBN Classification
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