Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/65882

TitlePredicting the length of hospital stay after surgery for perforated peptic ulcer
Author(s)Machado, José Manuel
Cardoso, Ana Catarina
Gomes, Inês Varela
Silva, Inês
Lopes, Vítor
Peixoto, Hugo
Abelha, António
KeywordsClassification
CRISP-DM
Data mining
Decision Support Systems
Length of hospital stay
Perforated peptic ulcer
Issue date2019
PublisherSpringer-Verlag
JournalAdvances in Intelligent Systems and Computing
Abstract(s)The management of peptic ulcer disease usually implies an urgent surgical procedure with the need of a patient’s hospital admission. By predicting the length of hospital stay of patients, improvements can be made regarding the quality of services provided to patients. This paper focuses on using real data to identify patterns in patients’ profiles and surgical events, in order to predict if patients will need hospital care for a shorter or longer period of time. This goal is pursued using a Data Mining process which follows the CRISP-DM methodology. In particular, classification models are built by combining different scenarios, algorithms and sampling methods. The data mining model which performed best achieved an accuracy of 87.30%, a specificity of 89.40%, and a sensitivity of 81.30%, using JRip, a rule-based algorithm and Cross Validation as a sampling method.
TypeConference paper
URIhttp://hdl.handle.net/1822/65882
ISBN9783030118891
DOI10.1007/978-3-030-11890-7_55
ISSN2194-5357
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CAlg - Livros e capítulos de livros/Books and book chapters

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