The reliability of classification of terminal nodes in GUIDE decision tree to predict the nonalcoholic fatty liver disease

DOI10.1155/2016/3874086zbMath1423.92072OpenAlexW2559950204WikidataQ39039524 ScholiaQ39039524MaRDI QIDQ2013940

Seyyed Mohammad Taghi Ayatollahi, Saeedeh Pourahmad, Mehdi Birjandi

Publication date: 10 August 2017

Published in: Computational \& Mathematical Methods in Medicine (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2016/3874086

zbMATH Keywords

classification of terminal nodesGUIDE decision treenonalcoholic fatty liver disease

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Medical applications (general) (92C50)

Uses Software

• AdaBoost.MH

Cites Work

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• Bagging predictors
• Boosting algorithms: regularization, prediction and model fitting
• Improving the precision of classification trees
• A decision-theoretic generalization of on-line learning and an application to boosting
• Analyzing bagging
• Fifty Years of Classification and Regression Trees
• Random forests

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