Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/51687| Título: | Predicting the need of Neonatal Resuscitation using Data Mining |
| Autor(es): | Morais, Ana Peixoto, Hugo Daniel Abreu Coimbra, Ana Cecília Sousa Rocha Abelha, António Machado, José Manuel |
| Palavras-chave: | Classification CRISP-DM Data Mining Decision Support Systems Neonatal Resucitation |
| Data: | 2017 |
| Editora: | Elsevier |
| Revista: | Procedia Computer Science |
| Resumo(s): | It is estimated that approximately 10% of newborns require some kind of assistance for breathing at birth. Aiming to prevent neonatal mortality, the goal behind this paper is to predict the need for neonatal resuscitation given some health conditions of both the newborn and the mother, and also the characteristics of the pregnancy and the delivery using Data Mining (DM) models induced with classification techniques. During the DM process, the CRISP-DM Methodology was followed and the WEKA software tool was used to induce the DM models. For some models, it was possible to achieve sensitivity results higher than 90% and specificity and accuracy results superior to 98%, which were considered to be satisfactory. |
| Tipo: | Artigo em ata de conferência |
| Descrição: | "International Workshop on Healthcare Interoperability and Pervasive Intelligent Systems (HiPIS 2017)" |
| URI: | https://hdl.handle.net/1822/51687 |
| DOI: | 10.1016/j.procs.2017.08.287 |
| ISSN: | 1877-0509 |
| Arbitragem científica: | yes |
| Acesso: | Acesso aberto |
| Aparece nas coleções: |
