Multistage SVM as a Clinical Decision Making Tool for Predicting Post Operative Patient Status
نویسندگان
چکیده
Because applying machine learning techniques in support of clinical decision would improve decision makers in healthcare, we present in this paper a comparative framework of Support Vector Machine (SVM) classifiers based on post operative patient (POP) data. We compare the performance of a single multiclass SVM and a multistage SVM (MSVM) to those obtained by a number of other classifiers presented in the literature and show that both SVM approaches significantly outperform the other methods resulting in 84.4% and 94.3% overall accuracy respectively. Results for the non-SVM classifiers ranged from 48% 77.7% accuracy.
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