Model Performance
Three ML models (Logistic Regression, Random Forest, MLP) trained on TCGA RNA-seq data with 5-fold stratified cross-validation.
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Best Accuracy
bal. accuracy
—
Best AUC
across cancers
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Avg Specificity Gain
percentage points
5
Cancer Types
TCGA cohorts
3
Models
LR · RF · MLP
Specificity Improvements by Cancer Type
MLP Performance Dashboard
| Cancer | Bal. Accuracy | Specificity | Sensitivity | AUC | MCC | Architecture | Samples (T/N) |
|---|---|---|---|---|---|---|---|
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Task × Model Results
| Task | Model | Accuracy | Precision | Recall | ROC AUC |
|---|---|---|---|---|---|
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Limitations
- Near-perfect AUC (0.99+) reflects the intrinsic separability of tumor vs. normal transcriptomes, not signature-specific discriminatory power. Random gene sets of similar size may achieve comparable performance.
- PRAD specificity (73.5%) is the lowest across cancers. Prostate adjacent-normal tissue is known to contain tumor cell contamination, creating a harder classification boundary. PRAD results should be considered exploratory.
- UCEC has only 201 samples (smallest dataset) yet achieves AUC = 1.000. This should be interpreted with caution given the small test set size per fold.
- SMOTE oversampling is applied for PRAD and BLCA within CV folds. Class weighting may be more appropriate for high-dimensional data.
All metrics are averaged over 5-fold stratified cross-validation.
Architecture is selected dynamically: 512→256→128 for datasets with
n > 600 samples, 256→128 for smaller datasets.