Name
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-21024-7_29 | MLDM |
Keywords | Field | DocType |
Classification problems,Approach instance-based,Area under the roc curve | Decision tree,Data mining,Pattern recognition,Computer science,Artificial intelligence,Area under the roc curve,Machine learning,Patient-Specific Modeling | Conference |
Volume | ISSN | Citations |
9166 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 13 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guilherme Alberto Sousa Ribeiro | 1 | 0 | 0.34 |
| Alexandre César Muniz De Oliveira | 2 | 83 | 8.30 |
| Ferreira, Antonio L. | 3 | 1 | 0.91 |
| Shyam Visweswaran | 4 | 231 | 30.47 |
| Gregory F. Cooper | 5 | 3464 | 580.16 |