Osteoarthritis Disease Prediction Based on Random Forest

Osteoarthritis is a disease of knee joint, indicated from the biochemical changes and thinning of the knee joint cartilage, which can be seen using T2Map MRI and Density-weighted Protons sequence. These tools detect the thickness changes that occur in the cartilage layers which can identify the presence of osteoarthritis and its severity. However, the immediacy of the result of these tools, whether the patient has osteoarthritis or not, is quite low. This paper presents the classification of osteoarthritis disease into three classes of severity using the random forest method. This model can be used to predict the accuracy of osteoarthritis data by 86,96% in diagnosing the disease. The data of 33 patients with osteoarthritis in Cipto Mangunkusumo National Hospital of Indonesia were used.