Attribute Interactions in Medical Data Analysis

There is much empirical evidence about the success of naive Bayesian classification (NBC) in medical applications of attribute-based machine learning. NBC assumes conditional independence between attributes. In classification, such classifiers sum up the pieces of classrelated evidence from individual attributes, independently of other attributes. The performance, however, deteriorates significantly when the “interactions” between attributes become critical. We propose an approach to handling attribute interactions within the framework of “voting” classifiers, such as NBC. We propose an operational test for detecting interactions in learning data, and a procedure that takes into account the detected interactions in learning. This approach induces a structuring of the domain of attributes, may lead to improved classifier’s performance and may provide useful novel information for the domain expert when interpreting the results of learning. We report on its application in data analysis and model construction for the prediction of clinical outcome in hip arthroplasty.