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Developing Decision Tree Using ID3 for Depression Testing

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dc.contributor.author Aye, Theingi
dc.contributor.author Phyu, Aye Lei Lei
dc.date.accessioned 2019-07-12T04:02:52Z
dc.date.available 2019-07-12T04:02:52Z
dc.date.issued 2010-12-16
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/814
dc.description.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. en_US
dc.language.iso en en_US
dc.publisher Fifth Local Conference on Parallel and Soft Computing en_US
dc.subject Classification en_US
dc.subject Decision Tree en_US
dc.subject ID3 algorithm en_US
dc.title Developing Decision Tree Using ID3 for Depression Testing en_US
dc.type Article en_US


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