Name
Abstract | ||
|---|---|---|
In epidemiology, a standard way of constructing models is to conduct univariate analysis of independent variables, followed by fitting a multivariate logistic model to the selected features. The main measure for choosing a particular model is a goodness of fit criterion on a given dataset. While this measure indicates how well the model fits the data, it has little relation to the predictive accuracy of the model and therefore may not generalize beyond the given dataset. This aspect is not frequently considered in epidemiology. We suggest using modern machine learning methods for constructing and validating epidemiological models. The resulting models can be used to confirm epidemiologist's models or to suggest possible improvements. These approaches also provide estimates and confidence measures for the parameters and the predictive ability of the model. |
Year | Venue | Keywords |
|---|---|---|
2004 | DISCRETE METHODS IN EPIDEMIOLOGY | support vector machines, feature selection, cross validation |
Field | DocType | Volume |
Family medicine,Computer science,Epidemiology,Veterinary medicine | Conference | 70 |
ISSN | Citations | PageRank |
1052-1798 | 0 | 0.34 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dmitriy Fradkin | 1 | 344 | 19.25 |
| Ilya Muchnik | 2 | 323 | 47.03 |
| Patrick Hermans | 3 | 0 | 0.34 |
| Kenton Morgan | 4 | 0 | 0.68 |