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

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

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

Conference proceedings International

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

Pages: 126-130

Indexing

Scopus - 7 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific