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Diagnosis of Hypothyroid Disease by Using Decision Tree Induction
http://hdl.handle.net/20.500.12678/0000004183
http://hdl.handle.net/20.500.12678/00000041836fd7b8d1-a540-4317-951a-b88eee067dc2
bd37568e-afd0-403d-812c-49b4cb3f76e0
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55334.pdf (270 Kb)
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Article | ||||||
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Publication | ||||||
Title | ||||||
Title | Diagnosis of Hypothyroid Disease by Using Decision Tree Induction | |||||
Language | en | |||||
Publication date | 2009-12-30 | |||||
Authors | ||||||
Htut, May Zin | ||||||
Win, Thin Zar | ||||||
Description | ||||||
Data mining is the process of discovering usefulinformation underlying the data. Powerfultechniques are needed to extract patterns from largedata because traditional statistical tools areefficient enough any more. Classification andprediction are two forms of data analysis that canbe used predict future data trends. In this paper, weproposed a diagnosis system for Hypothyroiddisease by using decision tree induction algorithm.Decision tree induction algorithms have been usedfor classification in a wide range of applicationdomains. Decision trees are potentially powerfulpredictors and explicitly represent the structure of adataset. In this system, C4.5 classification algorithmis analyzed to build the model from a given set oftraining data and then produced classification rules.It examines the patient is positive or negative inhypothyroid. The performance evaluation of oursystem is also discussed in this paper. | ||||||
Keywords | ||||||
Data mining, Classification, Decision Tree Induction, Cross Validation method | ||||||
Identifier | http://onlineresource.ucsy.edu.mm/handle/123456789/1879 | |||||
Journal articles | ||||||
Fourth Local Conference on Parallel and Soft Computing | ||||||
Conference papers | ||||||
Books/reports/chapters | ||||||
Thesis/dissertations |