Decision Tree Applications for Data Modelling

Many organisations, nowadays, have developed their own databases, in which a large amount of valuable information, e.g., customers’ personal profiles, is stored. Such information plays an important role in organisations’ development processes as it can help them gain a better understanding of customers’ needs. To effectively extract such information and identify hidden relationships, there is a need to employ intelligent techniques, for example, data mining. Data mining is a process of knowledge discovery (Roiger & Geatz, 2003). There are a wide range of data mining techniques, one of which is decision trees. Decision trees, which can be used for the purposes of classifications and predictions, are a tool to support decision making (Lee et al., 2007). As a decision tree can accurately classify data and make effective predictions, it has already been employed for data analyses in many application domains. In this paper, we attempt to provide an overview of the applications that decision trees can support. In particular, we focus on business management, engineering, and health-care management. The structure of the paper is as follows. Firstly, Section 2 provides the theoretical background of decision trees. Section 3 then moves to discuss the applications that decision trees can support, with an emphasis on business management, engineering, and health-care management. For each application, how decision trees can help identify hidden relationships is described. Subsequently, Section 4 provides a critical discussion of limitations and identifies potential directions for future research. Finally, Section 5 presents the conclusions of the paper.