Juvenile Idiopathic Arthritis (JIA) is the most common rheumatic disease of childhood. Our objective is to predict the results of remission so that those children who are likely to experience poor remission outcomes could benefit from early aggressive treatment. Many classification techniques could provide either a binary prediction or an estimated probability of remission. However, parents would like to know more specifically about the remission outcomes of children similar to their own. In this project, we propose a supervised clustering method that provides this information. Inspired by the basic idea of supervised principal component analysis, we perform supervision by selecting and/or weighting explanatory variables differently depending on their associations with the class response. Our supervised clustering method is applied to JIA data and to data simulated with known properties. Our method is shown to be competitive with an existing supervised clustering method, classification trees and random forests in terms of out-of-sample misclassification rates.
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