INVESTIGATION OF CONTINUOUS MONITORING OF RHEUMATOID ARTHRITIS

Rheumatoid arthritis (RA) is a chronic autoimmune condition characterized by inflammation of the joints, leading to pain, swelling, and potential deformities. Early detection and accurate prediction of disease progression are critical for effective treatment and improved patient outcomes. Given the complexity of RA, traditional diagnostic methods often fall short in capturing subtle patterns indicative of disease onset and progression. Machine learning (ML) techniques offer a promising approach to addressing these challenges by analyzing patient data to detect RA with high accuracy.

This study focuses on developing an advanced ML-based system for RA detection and prediction. Leveraging a comprehensive dataset comprising clinical records, laboratory test results, and patient-reported outcomes, we trained and evaluated a hybrid model combining Long ShortTerm Memory (LSTM) networks for sequential data analysis and Gradient Boosting algorithms for feature refinement. The proposed system excels in capturing temporal dependencies in patient health trends while enhancing model robustness and accuracy.

To further support healthcare practitioners, an interactive web application was created, enabling users to input patient data and receive predictions regarding RA diagnosis and progression. The trained model achieved an accuracy of 98%, as measured by key metrics such as R² score, Mean Absolute Error (MAE), and precision-recall. This platform provides an intuitive and efficient tool for clinicians, significantly improving decision-making in RA management.

Keywords: Rheumatoid Arthritis, LSTM, Gradient Boosting, Time-Series Analysis, ML, DL, Patient Health Trends, RA Detection, Healthcare Technology, Web Application, Predictive Analytics.

Rheumatoid Arthritis LSTM Gradient Boosting Time-Series Analysis ML DL Patient Health Trends RA Detection Healthcare Technology Web Application Predictive Analytics.