Table 2 Comparative performance results in ovarian cancer prediction. Sensitivities and specificities were estimated by 100 two-fold cross-validations (standard deviations are in brackets).

Proposed method M = 3, Δθ = 5⁰

λ = 10^-4λ_max

Linear SVM

Sensitivity: 96.2 (2.7)%; specificity: 93.6 (4.1)%; accuracy: 94.9%

Control specific component extracted with respect to a cancer reference sample.

Proposed method M = 4, Δθ = 3⁰

λ = 10^-6λ_max

Linear SVM

Sensitivity: 95.4 (3)%; specificity: 94 (3.7)%; accuracy:94.7%

Control specific component extracted with respect to a cancer reference sample.

[1]

Sensitivity: 81.4 (7.1)%; specificity: 71.7 (6.6)%

[42]

Sensitivity: 100%; specificity: 95% (one partition only: 50/50 training; 66/50 test).

[44]

Accuracy averaged over 10 ten-fold partitions: 98-99% (sd: 0.3-0.8)

[13]

Sensitivity: 98%, specificity: 95%, two-fold CV with 100 partitions.

[45]

Average error rate of 4.1% with three-fold CV.