@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},