Classification Techniques to Predict Rheumatoid Arthritis Using Machine Learning – A Review

It seems like you've provided a brief overview of the challenges associated with early diagnosis of Rheumatoid Arthritis (RA) and mentioned the limitations of the current system that uses a convolutional neural network. You also suggest a computer-aided decision support approach based on Harmony Search to address these issues and support clinicians and technicians in early disease prognosis. Harmony Search is a met heuristic optimization algorithm inspired by the musical improvisation process. It's interesting to see its application in the context of developing a decision support system for RA diagnosis. Using such algorithms can potentially enhance the adaptability and flexibility of the system, allowing for better reusability and modification of acquired outcomes. However, it would be helpful to provide more details about the proposed Harmony Search-based approach, how it differs from the current convolutional neural network system, and the specific advantages it brings to the early prognosis of RA. Additionally, discussing the existing literature and research studies that support the effectiveness of Harmony Search in medical decision support systems would strengthen your argument.