Developing Decision Tree Using ID3 for Depression Testing

Abstract

Data classification is the process of building a model from available data called the training dataset and classifying object according to their attributes. A derived model may be represented in various forms, such as decision tree, mathematical formula and neural networks. Decision tree algorithm has been used for classification in a wide range of application domains. In this paper, we propose a new system that tests the level of depression by using decision tree induction classification algorithm. Depending upon the data tuples of patient’s symptom dataset, the system can classify the level of depression within a minimum of time. This system will be implemented by using C#(2005) and SQLSever 2000.