Comparison of accuracy values of biomedical data with different applications decision tree method

Data mining is used in many different areas. But the most important of these is the use of biomedical data in data mining. The results obtained from these data contribute to the pre-detection, prevalence and treatment of the diseases. Decision trees are one of the important tools that enable accurat...

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Bibliographic Details
Published in:	2018 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT) pp. 1 - 4
Main Authors:	Yildiz, Omer, Dayanan, Teoman, Argun, Doktor Ogretim Uyesi Irem Duzdar
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-04-2018
Subjects:	Bioinformatics biomedical data C4.5 Classification algorithms Data mining decision tree Decision trees Diseases Heart Nanoelectromechanical systems
Online Access:	Get full text
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Summary:	Data mining is used in many different areas. But the most important of these is the use of biomedical data in data mining. The results obtained from these data contribute to the pre-detection, prevalence and treatment of the diseases. Decision trees are one of the important tools that enable accurate assessment of these biomedical data. There are many applications that use decision trees such as Weka, R, Rapid Miner, Knime and Orange. Whether these practices correctly apply decision trees is of particular importance in terms of biomedical data. In this study, biomedical datasets taken from UCI Machine Learning Repository were classified by C4.5 algorithm. Data mining applications, which are widely used in the market for classification, have been selected. The accuracy parameter is used for the analysis. This study aims to reach the conclusion which application will give more accurate results about not analyzed biomedical datasets.
DOI:	10.1109/EBBT.2018.8391439