Predictive models for bariatric surgery risks with imbalanced medical datasets

DOI10.1007/s10479-019-03156-8zbMath1494.62026OpenAlexW2745117980MaRDI QIDQ2288869

Joseph Ewing, Ilya Safro, Talayeh Razzaghi, Ehsan Sadrfaridpour, John D. Scott

Publication date: 20 January 2020

Published in: Annals of Operations Research (Search for Journal in Brave)

Full work available at URL: https://tigerprints.clemson.edu/cgi/viewcontent.cgi?article=1032&context=computing_pubs

zbMATH Keywords

risk predictionimbalanced dataclinical decision supportbariatric surgery

Mathematics Subject Classification ID

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

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Uses Software

• randomForest
• C4.5
• SMOTE

Cites Work

• Unnamed Item
• Bagging predictors
• Weighted relaxed support vector machines
• Optimizing feature selection to improve medical diagnosis
• Feature Subset Selection by Bayesian network-based optimization
• Classifying readmissions to a cardiac intensive care unit
• Coronary risk prediction by logical analysis of data
• Evaluation Measures of the Classification Performance of Imbalanced Data Sets
• The elements of statistical learning. Data mining, inference, and prediction
• Random forests

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