Generating decision trees from otoneurological data with a variable grouping method

When medical data sets are modelled by machine learning methods, wealth of variables may be available. This paper deals with variable selection for decision tree induction in the context of two otoneurological data sets: vertigo data, and postoperative nausea and vomiting data. First, a variable grouping method based on measures of association and graph theoretic techniques was used to gain insight into data. Then, representations of learning data were defined using the information from discovered variable groups, and decision trees were generated. The use of variable grouping method was beneficial by revealing interesting associations between variables and enabling generation of accurate and reasonable decision trees that modelled the application areas from different viewpoints.