title: Improving detection of apneic events by learning from examples and treatment of missing data.
abstract: This paper presents a comparative study over the respiratory pattern classification task involving three missing data imputation techniques, and four different machine learning algorithms. The main goal was to find a classifier that achieves the best accuracy results using a scalable imputation method in comparison to the method used in a previous work of the authors. The results obtained show that the Self-organization maps imputation method allows any classifier to achieve improvements over the rest of the imputation methods, and that the Feedforward neural network classifier offers the best performance regardless the imputation method used.
authors.affiliation: Department of Computer Science, Faculty of Informatics, University of A Coruña, Campus de Elviña s/n, 15071 A Coruña, Spain.
authors.forename: Elena
authors.initials: E
authors.lastname: Hernández-Pereira
authors.affiliation: Sleep Center, Medisch Centrum Haaglanden, Lijnbaan 32, 2512VA The Hague, The Netherlands.
authors.forename: Diego
authors.initials: D
authors.lastname: Alvarez-Estévez
authors.affiliation: Department of Computer Science, Faculty of Informatics, University of A Coruña, Campus de Elviña s/n, 15071 A Coruña, Spain.
authors.forename: Vicente
authors.initials: V
authors.lastname: Moret-Bonillo
mesh_terms: D003625:Data Collection / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000592:standards*; D057286:Electronic Health Records / Q000592:standards*; D006801:Humans; D012891:Sleep Apnea Syndromes / Q000175:diagnosis* / Q000628:therapy*