Comparative Analysis of Machine Learning Classifiers for Differentially Methylated Gene Classification in Rheumatoid Arthritis

Rheumatoid Arthritis (RA), an autoimmune disorder constantly attacks the joints. This Research delves into the molecular mysteries of RA, with a particular interest in Differentially Methylated Genes (DMGs). These genes hold clues to how the disease starts, progresses, and how severe it might become. A massive DMG dataset of RA patients from the Rheumatoid Arthritis Bioinformatics Centre (RABC) was used. By analyzing them, people at risk for RA much earlier can potentially be identified. Different Machine Learning (ML) tools are put to the test Random Forest, AdaBoost, Extra Trees, Decision Tree, and Linear Discriminant Analysis. To find the best ML detective to sniff out these DMGs with the highest accuracy. Understanding DMGs goes beyond early diagnosis. Using the power of ML, breakthroughs in RA treatment can be unlocked in future.