Table 2.
Performance comparison of GARSL with other well-known machine learning models to predict early recurrence using both clinical and radiomic features
| Method | 10-CV (n = 362) |
Test set (n = 155) |
|||
|---|---|---|---|---|---|
| accuracy, % | MCC | accuracy, % | MCC | AUC | |
| GARSL | 69.89 | 0.476 | 72.90 | 0.453 | 0.739 |
| C4.5 | 56.35 | 0.119 | 57.42 | 0.172 | 0.610 |
| Random tree | 51.93 | 0.038 | 61.29 | 0.228 | 0.615 |
| Hoeffding tree | 61.33 | 0.216 | 68.39 | 0.361 | 0.718 |
| Logistic model tree | 63.54 | 0.262 | 67.74 | 0.346 | 0.716 |
| Logistic regression | 54.14 | 0.085 | 60.65 | 0.212 | 0.642 |
| Naive Bayes | 61.05 | 0.211 | 67.10 | 0.335 | 0.716 |
Formulas, indexes, and modeling details of every method are described in the online suppl. File. CV, cross-validation; MCC, Matthews correlation coefficient; AUC, area under the receiver operating characteristic curve; GARSL, genetic algorithm for predicting recurrence after surgery of liver cancer.