Clinical Variable Relationship Evaluation using Decision Tree Rule Extraction
Evaluating associations and relationships between variable is a very challenging and important problem in the domain of medicine and clinical data analysis. Not many classification methods have been tried in the literature to tackle this problem. Decision Tree is one of the most important data mining methods that can be used, due to their property of implicitly performing variable screening or feature selection and their requirement of relatively little effort from users for data preparation. However, a major drawback associated with the use of decision tree for decision making is their lack of interpret-able capability specially when using tools like Weka. Though decision trees can achieve a high predictive accuracy rate, the reasoning behind how they reach their decisions is not readily available. But this problem can be handled very easily if the decision tree can be utilized by extracting their rules and analyzing these rules. In this paper we present an approach for extracting rules from the decision tree which can be utilized for determining relationship between clinical variables. Furthermore, we also discuss how these rules can be visualized in a compact and intuitive tabular format that facilitates easy analysis. It is concluded that decision tree rule extraction can be considered as powerful analysis tools that allow us to facilitate analysis of clinical variables and its association.