This research aims to develop an intelligent Ayurvedic medicine system for the early identification and successful treatment of arthritis. The proposed system consists of four major components: X-Ray Image Analysis, Blood Report Monitoring, Ayurvedic Treatment Recommendation, and Continuous Monitoring and Feedback. In the X-Ray Image Analysis component, the K-Nearest Neighbor (KNN) technique is utilized to analyze X-Ray images and classify them as indicative of arthritis and its severity. The Blood Report Monitoring component employs a Long Short-Term Memory (LSTM) algorithm to analyze time-series data from blood test results and predict the progression of arthritis. Based on Ayurvedic medical principles, the Ayurvedic Treatment Recommendation component generates specific treatment suggestions considering the patient's age, gender, symptoms, and overall health, utilizing a decision tree algorithm. For continuous monitoring, the XG Boost algorithm is employed in the Continuous Monitoring and Feedback component, allowing real-time monitoring of the patient's symptoms, treatment progress, and general health. This facilitates timely interventions and modifications by the treating clinician. The proposed system integrates advanced technology with traditional Ayurvedic medicine knowledge, providing a comprehensive solution for the early detection and successful management of arthritis.

The development of this AyuAIra intelligent Ayurvedic medicine system has the potential to significantly improve arthritis care by providing accurate and personalized assessments, recommendations, and continuous monitoring using machine learning algorithms. Future studies will focus on further algorithm development, effective clinical validation, and integration of the system into healthcare settings to evaluate its real-world efficiency.