@article{(Open Science Index):https://publications.waset.org/pdf/10003391, title = {Patient-Specific Modeling Algorithm for Medical Data Based on AUC}, author = {Guilherme Ribeiro and Alexandre Oliveira and Antonio Ferreira and Shyam Visweswaran and Gregory Cooper}, country = {}, institution = {}, abstract = {Patient-specific models are instance-based learning 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 patient-specific decision path (PSDP) entropy-based and CART methods, 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.}, journal = {International Journal of Computer and Information Engineering}, volume = {10}, number = {1}, year = {2016}, pages = {105 - 110}, ee = {https://publications.waset.org/pdf/10003391}, url = {https://publications.waset.org/vol/109}, bibsource = {https://publications.waset.org/}, issn = {eISSN: 1307-6892}, publisher = {World Academy of Science, Engineering and Technology}, index = {Open Science Index 109, 2016}, }