Early detection of rheumatoid arthritis in adults.

Early detection of rheumatoid arthritis (RA) is crucial for timely intervention and effective management of the disease, which can significantly reduce the risk of joint damage and improve patient outcomes. This study presents a machine learning-based framework for predicting the early risk of RA in adults using clinical and laboratory data. The dataset consists of key patient features such as age, gender, clinical notes, symptom duration, Erythrocyte Sedimentation Rate (ESR), C-Reactive Protein (CRP), Rheumatoid Factor (RF) status, and Anti-Cyclic Citrullinated Peptide (Anti-CCP) status. The target variable, Early_RA_Risk, classifies patients into three categories: High, Medium, and Low risk. Various machine learning algorithms, including Convolutional Neural Networks (CNN), Random Forest, XGBoost, and LightGBM, are employed to train the predictive models. The study aims to evaluate the accuracy and performance of these models in classifying patients based on their likelihood of developing rheumatoid arthritis. By leveraging data-driven insights, this framework aims to assist healthcare professionals in identifying at-risk individuals, enabling early intervention and better management strategies. The results highlight the potential of machine learning in improving the early detection and prognosis of rheumatoid arthritis, contributing to more personalized and effective treatment plans for patients.

KEYWORDS:  Early detection of rheumatoid arthritis (RA) using machine learning techniques, including Random Forest, XGBoost, LightGBM, and CNN, classifies patients based on clinical data like ESR, CRP, and Anti-CCP status. The goal is to predict Early_RA_Risk (High, Medium, Low) for timely intervention. This approach enhances disease prediction and personalized healthcare strategies for RA management.