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 |
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| Format: | Conference Proceeding |
| Language: | English |
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IEEE
06.06.2025
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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. |
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| 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 |
| Author_xml | – sequence: 1 givenname: Jayashree surname: L K fullname: L K, Jayashree email: jayashreelk@kssem.edu.in organization: Dayananda Sagar University Harohalli,India – sequence: 2 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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