Impact of Imputing Missing Data in Bayesian Network Structure Learning for Obstructive Sleep Apnea Diagnosis

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2018

Autores

Santos, DF (Autor) FMUP; Matilde Soares (Autor) FMUP; Pedro Pereira Rodrigues (Autor) FMUP

Ata de Conferência Internacional

Título: Building Continents of Knowledge in Oceans of Data: The Future of Co-Created eHealth

Páginas: 126-130

Indexação

Scopus - 8 Citações

Outras Informações

ID Authenticus: P-00N-XPN

DOI: 10.3233/978-1-61499-852-5-126

Abstract (EN): Numerous diagnostic decisions are made every day by healthcare professionals. Bayesian networks can provide a useful aid to the process, but learning their structure from data generally requires the absence of missing data, a common problem in medical data. We have studied missing data imputation using a step-wise nearest neighbors' algorithm, which we recommended given its limited impact on the assessed validity of structure learning Bayesian network classifiers for Obstructive Sleep Apnea diagnosis. © 2018 European Federation for Medical Informatics (EFMI) and IOS Press.

Idioma: Inglês

Tipo (Avaliação Docente): Científica