AMIA Annu Symp Proc - Predicting discharge mortality after acute ischemic stroke using balanced data.

Tópicos

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Resumo

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.

Resumo Limpo

sever model develop predict stroke outcom eg stroke mortal patient depend etc recent decad howev littl discuss regard problem betweenclass imbal stroke dataset lead predict bias decreas perform paper demonstr use synthet minor oversampl techniqu overcom problem also compar state art machin learn method construct sixvari support vector machin svm model predict stroke mortal discharg final discuss identif reduc featur set allow us identifi addit case research databas valid test classifi achiev cstatist crossvalid dataset demonstr good classif perform use reduc set variabl

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