Abstract
Patient-specific models are instance-based learn algorithms that take advantage of the particular features of the patient case at hand to predict an outcome. We introduce two patient-specific algorithms based on decision tree paradigm that use AUC as a metric to select an attribute. We apply the patient specific algorithms to predict outcomes in several datasets, including medical datasets. Compared to the standard entropy-based method, the AUC-based patient-specific decision path models performed equivalently on area under the ROC curve (AUC). Our results provide support for patient-specific methods being a promising approach for making clinical predictions.
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Ribeiro, G.A.S., de Oliveira, A.C.M., Ferreira, A.L.S., Visweswaran, S., Cooper, G.F. (2015). Patient-Specific Modeling of Medical Data. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2015. Lecture Notes in Computer Science(), vol 9166. Springer, Cham. https://doi.org/10.1007/978-3-319-21024-7_29
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DOI: https://doi.org/10.1007/978-3-319-21024-7_29
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