From: Application of machine learning techniques for predicting survival in ovarian cancer
Study | Dataset | Model | Class/group | Accuracy | AUC | F1-score | C-index | Explainability | |
---|---|---|---|---|---|---|---|---|---|
Chen [14] | SEER (4,128 samples) | L2-regularized logistic regression | Binary classification (survived more than 22 months, survived less than 22 months) | 0.761 | 0.621 | 0.216 | - | no | |
Grimley et al. [15] | SEER | Dataset 1 (39,514 samples) | Ensemble Algorithm for clustering Cancer Data (EACCD) | 9 epithelial ovarian carcinoma prognostic groups | - | - | - | 0.7391 | - |
Dataset 2 (25,291 samples, derived from dataset 1) | 0.7605 | ||||||||
This study | SEER (42,827 samples) | Random Forest | Multiclass classification (5 classes) | 0.887 | 0.823 | 0.717 | - | yes |