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https://hdl.handle.net/1822/65888
Título: | Data mining for prediction of length of stay of cardiovascular accident inpatients |
Autor(es): | Silva, Carlos César Loureiro Oliveira, Daniela Peixoto, Hugo Machado, José Manuel Abelha, António |
Palavras-chave: | Cardiovascular accident Data mining Prediction Weka |
Data: | 2018 |
Editora: | Springer Verlag |
Revista: | Communications in Computer and Information Science |
Resumo(s): | The healthcare sector generates large amounts of data on a daily basis. This data holds valuable knowledge that, beyond supporting a wide range of medical and healthcare functions such as clinical decision support, can be used for improving profits and cutting down on wasted overhead. The evaluation and analysis of stored clinical data may lead to the discovery of trends and patterns that can significantly enhance overall understanding of disease progression and clinical management. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a data mining project approach to predict the hospitalization period of cardiovascular accident patients. This provides an effective tool for the hospital cost containment and management efficiency. The data used for this project contains information about patients hospitalized in Cardiovascular Accident’s unit in 2016 for having suffered a stroke. The Weka software was used as the machine learning toolkit. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/65888 |
ISBN: | 9783030028428 |
DOI: | 10.1007/978-3-030-02843-5_43 |
ISSN: | 1865-0929 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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DTGS_26.pdf | 328,36 kB | Adobe PDF | Ver/Abrir |