Building an automated model to predict and detect early onset osteoarthritis

This research targets the need to provide early detection of osteoarthritis to assist the ongoing global effort to make treatment of this disease viable and effective. With regards to the rapid degradation of the condition, it is critical to detect it early enough so that suppressive treatment can reduce symptoms and progression. Using an approach of automation via efficient data analysis, it is possible to detect arthritis quite early in a patient’s diagnostics. This paper aims to provide both a statistical and a more direct approach to the determination of the probability of osteoarthritis at early onset to improve the odds for a patient. The system will take primary variables from a patient's file and look for patterns matching cases in the dataset it was trained against. The dataset for the same has been taken from the NIH archive for academic purposes.