# A Serum Metabolomic Signature for the Detection and Grading of Bladder Cancer
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & **S 1** & **Sp** & **PPV** & **NPV** & **PLR** & **NLR** & **A** \\ \hline \multirow{10}{*}{**NMIC**} & Decision Tree & 0.97 \(\pm\) 0.01 & 0.96 \(\pm\) 0.02 & 0.98 \(\pm\) 0.01 & 0.95 \(\pm\) 0.04 & 26.28 & 0.03 & 0.97 \\  & PLS-DA & 1.00 \(\pm\) 0.00 & 0.96 \(\pm\) 0.02 & 0.98 \(\pm\) 0.01 & 1.00 \(\pm\) 0.00 & 27.00 & 0.00 & 0.99 \\  & Naïve Bayes & 0.81 \(\pm\) 0.03 & 0.99 \(\pm\) 0.01 & 0.99 \(\pm\) 0.01 & 0.74 \(\pm\) 0.04 & 65.78 & 0.19 & 0.87 \\  & Random Forest & 0.97 \(\pm\) 0.01 & 0.98 \(\pm\) 0.02 & 0.99 \(\pm\) 0.01 & 0.95 \(\pm\) 0.02 & 39.41 & 0.03 & 0.97 \\  & k-NN & 1.00 \(\pm\) 0.00 & 1.00 \(\pm\) 0.00 & 1.00 \(\pm\) 0.00 & 1.00 \(\pm\) 0.00 & ND & 0.00 & 1.00 \\  & Logistic Regression & 1.00 \(\pm\) 0.00 & 1.00 \(\pm\) 0.00 & 1.00 \(\pm\) 0.00 & 1.00 \(\pm\) 0.00 & ND & 0.00 & 1.00 \\  & SVM & 1.00 \(\pm\) 0.00 & 0.98 \(\pm\) 0.02 & 0.99 \(\pm\) 0.01 & 1.00 \(\pm\) 0.00 & 40.50 & 0.00 & 0.99 \\  & Deep Learning & 1.00 \(\pm\) 0.00 & 0.99 \(\pm\) 0.01 & 0.99 \(\pm\) 0.01 & 1.00 \(\pm\) 0.01 & 81.00 & 0.00 & 1.00 \\  & **Ensemble** & **1.00 \(\pm\) 0.00** & **0.98 \(\pm\) 0.02** & **0.99 \(\pm\) 0.01** & **1.00 \(\pm\) 0.00** & **40.50** & **0.00** & **0.99** \\ \cline{2-10}  & Decision Tree & 0.94 \(\pm\) 0.03 & 0.85 \(\pm\) 0.05 & 0.92 \(\pm\) 0.03 & 0.89 \(\pm\) 0.04 & 6.44 & 0.07 & 0.91 \\  & PLS-DA & 0.64 \(\pm\) 0.05 & 0.89 \(\pm\) 0.04 & 0.91 \(\pm\) 0.04 & 0.60 \(\pm\) 0.05 & 5.96 & 0.41 & 0.73 \\  & Naïve Bayes & 0.46 \(\pm\) 0.05 & 0.93 \(\pm\) 0.04 & 0.91 \(\pm\) 0.04 & 0.50 \(\pm\) 0.05 & 6.29 & 0.59 & 0.63 \\  & Random Forest & 0.98 \(\pm\) 0.01 & 0.56 \(\pm\) 0.07 & 0.79 \(\pm\) 0.04 & 0.94 \(\pm\) 0.04 & 2.24 & 0.04 & 0.83 \\  & k-NN & 1.00 \(\pm\) 0.00 & 1.00 \(\pm\) 0.00 & 1.00 \(\pm\) 0.00 & 1.00 \(\pm\) 0.00 & ND & 0.00 & 1.00 \\  & aNN & 0.99 \(\pm\) 0.01 & 0.35 \(\pm\) 0.06 & 0.72 \(\pm\) 0.04 & 0.95 \(\pm\) 0.05 & 1.51 & 0.03 & 0.75 \\  & Logistic Regression & 1.00 \(\pm\) 0.00 & 1.00 \(\pm\) 0.00 & 1.00 \(\pm\) 0.00 & 1.00 \(\pm\) 0.00 & ND & 0.00 & 1.00 \\  & SVM & 0.98 \(\pm\) 0.01 & 0.27 \(\pm\) 0.06 & 0.70 \(\pm\) 0.04 & 0.88 \(\pm\) 0.08 & 1.35 & 0.08 & 0.72 \\  & Deep Learning & 0.86 \(\pm\) 0.04 & 0.80 \(\pm\) 0.05 & 0.88 \(\pm\) 0.03 & 0.77 \(\pm\) 0.06 & 4.31 & 0.17 & 0.84 \\  & **Ensemble** & **0.79 \(\pm\) 0.06** & **0.83 \(\pm\) 0.07** & **0.87 \(\pm\) 0.05** & **0.73 \(\pm\) 0.08** & **4.56** & **0.26** & **0.80** \\ \cline{2-10}  & Decision Tree & 1.00 \(\pm\) 0.00 & 0.20 \(\pm\) 0.13 & 0.93 \(\pm\) 0.03 & 1.00 \(\pm\) 0.00 & 1.25 & 0.00 & 0.94 \\  & PLS-DA & 0.51 \(\pm\) 0.05 & 0.50 \(\pm\) 0.11 & 0.86 \(\pm\) 0.04 & 0.15 \(\pm\) 0.04 & 1.03 & 0.97 & 0.51 \\  & Naïve Bayes & 0.49 \(\pm\) 0.05 & 0.90 \(\pm\) 0.07 & 0.97 \(\pm\) 0.02 & 0.24 \(\pm\) 0.05 & 4.91 & 0.57 & 0.55 \\  & Random Forest & 1.00 \(\pm\) 0.00 & 0.00 \(\pm\) 0.00 & 0.85 \(\pm\) 0.03 & ND & 1.00 & ND & 0.85 \\  & k-NN & 1.00 \(\pm\) 0.00 & 1.00 \(\pm\) 0.00 & 1.00 \(\pm\) 0.00 & 1.00 \(\pm\) 0.00 & ND & 0.00 & 1.00 \\  & aNN & 1.00 \(\pm\) 0.00 & 0.30 \(\pm\) 0.10 & 0.89 \(\pm\) 0.03 & 1.00 \(\pm\) 0.00 & 1.43 & 0.00 & 0.90 \\  & Logistic Regression & 1.00 \(\pm\) 0.00 & 1.00 \(\pm\) 0.00 & 1.00 \(\pm\) 0.00 & 1.00 \(\pm\) 0.00 & ND & 0.00 & 1.00 \\  & SVM & 1.00 \(\pm\) 0.00 & 0.10 \(\pm\) 0.07 & 0.86 \(\pm\) 0.03 & 1.00 \(\pm\) 0.00 & 1.11 & 0.00 & 0.87 \\  & Deep Learning & 1.00 \(\pm\) 0.00 & 0.50 \(\pm\) 0.11 & 0.92 \(\pm\) 0.02 & 1.00 \(\pm\) 0.00 & 2.00 & 0.00 & 0.93 \\
**Ensemble** & **1.00 \(\pm\) 0.00** & **0.00 \(\pm\) 0.00** & **0.85 \(\pm\) 0.03** & **ND** & **1.00** & **ND** & **0.85** \\ \hline \hline \end{tabular}  
* S: Sensitivity; Sp: Specificity; PPV: Positive Prognostic Value; NPV: Negative Prognostic Value; PLR: Positive Likelihood Ratio; NLR: Negative Likelihood Ratio; A: Accuracy; PLS-DA: Partial Least Square Discriminant Analysis; k-NN: k-nearest neighbors; aNN: Artificial Neuronal Net; SVM: Support Vector Machine; ND: Not Determinable.  
\end{table}