Boosting Naive-Bayes classifiers to predict outcomes for hip prostheses

Our primary aim is to develop a classifier system that is capable of predicting the success or failure of hip prostheses on the basis of data from early radiological observations. The data set we employ (collected at The Royal London Hospital) records observations taken in the early years following fixation of the prosthesis and failure or otherwise after ten years. Many of the records contained in this data set have missing values. Recent work on the well-known Pima Indian data set has demonstrated the effectiveness of the Naive-Bayes (NB) method, coupled with boosting, on data with missing values. In this paper we investigate the performance of the NB method and boosting on the hip prosthesis data which contains a much greater proportion of missing values than the Pima Indian data. Our data set is additionally challenging in that it contains many more examples of one class (success) than the other