Table 2 Optimal results of Models 1–3 on the validation set
From: An artificial intelligence-based risk prediction model of myocardial infarction
Model name | Construction method | Optimal algorithm | Number of optimal feature | Negative training n sample | Positive training n sample | Negative validation sample | Positive validation sample | Validation accuracy | ValidationF1 score |
|---|---|---|---|---|---|---|---|---|---|
Model1 | Proportional division | GBDT | 9 | 175,496 | 10,756 | 43,873 | 2690 | 0.96 | 0.78 |
Model2 | Upsampling | RF | 3 | 175,395 | 172,209 | 43,974 | 42,927 | 0.99 | 0.99 |
Model3 | Downsampling | GBDT | 24 | 10,784 | 10,729 | 2662 | 2717 | 0.84 | 0.84 |