Prediction of Rheumatoid Arthritis Susceptibility Using Gene Mutation Rate

Rheumatoid Arthritis (RA) is an incurable condition which to the present day is an ailment without any cure. In this disease immune system of the body attacks its own healthy tissues perceiving them as foreign. RA without any cure causes damages to joints which are irreversible and very painful. The only solace to the affected patients who suffer from extreme pain is redemption with a few drugs. Disease onset prediction and further monitoring of disease activity are very crucial stages for RA.RA susceptibility has always had relationship with complex genetic characteristics. The proposed methodology emphasizes on early prediction of rheumatoid arthritis by applying the machine learning technique SVM based on HLA-DRB4 gene sequence and mutation rate. The technique facilitates in early prediction of rheumatoid arthritis by comparing pair-wise DNA nucleotide point mutation on one of the primary RA associated candidate gene HLA-DRB4 (which resides in chromosome location 6) with its mutated gene samples, thus calculating the mutation rate. On the basis of mutation rate value computed the sample bearing individual may be recommended to take up RA susceptibility test. This approach of applying linear kernel SVM machine learning technique on gene data substantially increases the chances of early disease prediction, which will be very beneficial to the healthcare community and people in general which in turn can prevent people from going through extreme pain and handicapped lifestyle as they can know beforehand whether they are prone to get rheumatoid arthritis.