Attribute Interactions in Medical Data Analysis

Aleks Jakulin and Ivan Bratko and Dragica Smrke and Janez Demšar and Blaz Zupan (2003) Attribute Interactions in Medical Data Analysis. In: 9th Conference on Artificial Intelligence in Medicine in Europe (AIME 2003), October 18-22, 2003, Protaras, Cyprus.

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Abstract

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 class-related 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 the detected interactions into account while learning. This approach induces a structuring of the domain of attributes, it 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.

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