title: Predicting discharge mortality after acute ischemic stroke using balanced data.
abstract: Several models have been developed to predict stroke outcomes (e.g., stroke mortality, patient dependence, etc.) in recent decades. However, there is little discussion regarding the problem of between-class imbalance in stroke datasets, which leads to prediction bias and decreased performance. In this paper, we demonstrate the use of the Synthetic Minority Over-sampling Technique to overcome such problems. We also compare state of the art machine learning methods and construct a six-variable support vector machine (SVM) model to predict stroke mortality at discharge. Finally, we discuss how the identification of a reduced feature set allowed us to identify additional cases in our research database for validation testing. Our classifier achieved a c-statistic of 0.865 on the cross-validated dataset, demonstrating good classification performance using a reduced set of variables.
authors.affiliation: Department of Bioengineering, University of California, Los Angeles, CA ; Medical Imaging Informatics, Department of Radiological Sciences, University of California, Los Angeles, CA.
authors.forename: King Chung
authors.initials: KC
authors.lastname: Ho
authors.affiliation: Department of Bioengineering, University of California, Los Angeles, CA ; Medical Imaging Informatics, Department of Radiological Sciences, University of California, Los Angeles, CA.
authors.forename: William
authors.initials: W
authors.lastname: Speier
authors.affiliation: Medical Imaging Informatics, Department of Radiological Sciences, University of California, Los Angeles, CA.
authors.forename: Suzie
authors.initials: S
authors.lastname: El-Saden
authors.affiliation: UCLA Stroke Center, Department of Neurology, University of California, Los Angeles, CA.
authors.forename: David S
authors.initials: DS
authors.lastname: Liebeskind
authors.affiliation: UCLA Stroke Center, Department of Neurology, University of California, Los Angeles, CA.
authors.forename: Jeffery L
authors.initials: JL
authors.lastname: Saver
authors.affiliation: Department of Bioengineering, University of California, Los Angeles, CA ; Medical Imaging Informatics, Department of Radiological Sciences, University of California, Los Angeles, CA.
authors.forename: Alex A T
authors.initials: AA
authors.lastname: Bui
authors.affiliation: Department of Bioengineering, University of California, Los Angeles, CA ; Medical Imaging Informatics, Department of Radiological Sciences, University of California, Los Angeles, CA.
authors.forename: Corey W
authors.initials: CW
authors.lastname: Arnold
mesh_terms: D001185:Artificial Intelligence; D001499:Bayes Theorem; D002545:Brain Ischemia / Q000401:mortality*; D003663:Decision Trees; D006801:Humans; D016015:Logistic Models; D015233:Models, Statistical; D010351:Patient Discharge; D020521:Stroke / Q000401:mortality*; D060388:Support Vector Machine