From: Accurate breast cancer diagnosis using a stable feature ranking algorithm
n | FR/SFS | Classifier | AUC | ACC | SEN | SPE | ||
---|---|---|---|---|---|---|---|---|
BCDR-F03 | [19] | 600 | SVM | 0.77±0.03 | ||||
[19] | 17 | SVM | 0.77±0.02 | |||||
[53] | 4 | elastic net | SVM | 0.69±0.05 | 0.74±0.05 | 0.56±0.10 | 0.81±0.08 | |
Ours | 4 | GFS | NB | 0.71±0.04 | 0.77±0.03 | 0.59±0.10 | 0.84±0.05 | |
WDBC | [54] | 24 | variable importance | hierarchical clustering RF | 0.9896 | 0.9705 | 0.9477 | 0.9841 |
[55] | 14 | genetic algorithm | particle swarm optimization | 0.966 | 0.975 | 0.937 | ||
[48] | 6 | genetic algorithm | kernel-based Bayesian | 0.994 | 0.971 | 0.924 | 1.000 | |
[56] | 14 | genetic algorithm | rotation forest | 0.993 | 0.9948 | |||
[57] | 9 | interaction dominance | 0.9966 | |||||
Ours | 2 | GFS | NB | 0.94±0.02 | 0.94±0.01 | 0.94±0.03 | 0.94±0.02 | |
GSE10810 | [22] | 8088 | false discovery rate | 1.000 | ||||
[49] | 80 | t-test | SVM | 0.7789 | ||||
Ours | 2 | GFS | SVM | 0.96±0.05 | 0.97±0.04 | 0.99±0.03 | 0.92±0.10 | |
GSE15852 | [23] | 33 | paired t-test | hierarchical cluster analysis | 0.88 | 0.86 | 0.91 | |
[51] | 10 | logistic regression | RF | 0.9311 | ||||
[52] | 50 | prioritization analysis | SVM | 0.87 | ||||
Ours | 4 | GFS | NB | 0.89±0.07 | 0.89±0.07 | 0.96±0.07 | 0.81±0.13 |