From: Machine learning approaches for predicting high cost high need patient expenditures in health care
Predicting objective | Model | Train | Test | ||||
---|---|---|---|---|---|---|---|
R-squared | RMSE | RMSE for Top 10% | R-squared | RMSE | RMSE for Top 10% | ||
PMPM | LR | 0.145 | 0.306 | 0.264 | 0.141 | 0.306 | 0.264 |
LASSO | 0.145 | 0.306 | 0.265 | 0.141 | 0.306 | 0.264 | |
GBM | 0.199 | 0.317 | 0.270 | 0.172 | 0.314 | 0.272 | |
RNN | 0.302 | 0.201 | 0.180 | 0.298 | 0.204 | 0.183 | |
logPMPM | LR | 0.401 | 0.306 | 0.265 | 0.402 | 0.306 | 0.264 |
LASSO | 0.401 | 0.306 | 0.265 | 0.402 | 0.306 | 0.264 | |
GBM | 0.399 | 0.316 | 0.267 | 0.394 | 0.314 | 0.269 | |
RNN | 0.445 | 0.220 | 0.184 | 0.442 | 0.223 | 0.187 | |
pctlPMPM | LR | 0.399 | 0.306 | 0.265 | 0.400 | 0.306 | 0.264 |
LASSO | 0.398 | 0.306 | 0.265 | 0.400 | 0.306 | 0.264 | |
GBM | 0.384 | 0.314 | 0.275 | 0.382 | 0.312 | 0.277 | |
RNN | 0.405 | 0.232 | 0.203 | 0.400 | 0.235 | 0.203 |