## 2 Methods
### Feature Combination Evaluation  
The last training set-up of ML models used the same approach with three dataset scenarios but with ANC feature combinations. The starting point was represented by the accuracy rank obtained in the standalone evaluation from Tables 7-9. The expectations of using combinations of two or more features is to increase the synergy for an ML model, thus the prediction rate should increase too. The results and metrics are shown in Tables 10-12. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Features** & **Model** & **Accuracy [\%]** & **Precision [\%]** & **Recall [\%]** & **Specificity [\%]** & **F1 Score** & **AUC** \\ \hline A, DtS & Coarse Tree & 69.9 & 58.9 & 66.7 & 71.1 & 0.63 & 0.65 \\ N, DtS & Fine Tree & 58.4 & 45.3 & 54.4 & 62.9 & 0.49 & 0.56 \\ NC, DtS & Logistic Regression & 58.9 & 53.7 & 55.4 & 65.4 & 0.55 & 0.59 \\ C, DtS & Ensemble & 58 & 45.3 & 57.3 & 63.9 & 0.51 & 0.61 \\ A, N & Coarse Tree & 66.2 & 63.2 & 61.2 & 71.1 & 0.62 & 0.64 \\ A, N, DtS & Coarse Tree & 67.6 & 58.9 & 66.7 & 71.1 & 0.63 & 0.65 \\ A, NC & Coarse Tree & 68 & 63.2 & 63.2 & 71.8 & 0.63 & 0.68 \\ A, NC, DtS & Coarse Tree & 68.5 & 58.9 & 66.7 & 71.1 & 0.63 & 0.65 \\ A, N, NC & Coarse Tree & 67.6 & 61.1 & 63.0 & 70.9 & 0.62 & 0.67 \\ A, N, NC, DtS & Coarse Tree & 68 & 58.9 & 66.7 & 71.1 & 0.63 & 0.66 \\ A, N, NC, C, DtS & Coarse Tree & 68 & 58.9 & 66.7 & 71.1 & 0.63 & 0.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: ML metrics using combination of ANC Test feature for Dataset 1. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Feature Selection** & **Model** & **Accuracy [\%]** & **Precision [\%]** & **Recall [\%]** & **Specificity [\%]** & **F1 Score** & **AUC** \\ \hline A & SVM Quadratic & 61.3 & 53.6 & 62.7 & 59.7 & 0.58 & 0.61 \\ N & SVM Cubic & 57.9 & 79.3 & 56.8 & 66.1 & 0.66 & 0.58 \\ NC & SVM Gaussian & 59.6 & 63.7 & 59.4 & 61.1 & 0.61 & 0.59 \\ C & Logistic Regression & 52.6 & 90.5 & 52.4 & 66.0 & 0.66 & 0.54 \\ DtS & Logistic Regression & 59.1 & 58.1 & 57.5 & 57.9 & 0.58 & 0.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: ML metric using ANC Test feature standalone for Merged dataset. Using a Gaussian dataset, the best accuracy of the feature combinations, rising up to 69.9%, was the A, DtS case as shown in Table 10 and Figure 5a. The F1 score did not increase more than 0.63 in the standalone evaluation within Dataset 1;just the AUC metric did. The other feature combinations scored around 68% accuracy with the same F1 score. Increasing the number of features to feed the ML model does not give a boost in performance since many selections represent a redundant role. Moving to Dataset 2, the best accuracy obtained of 72.9% comes from using 4/5 ANC parameters as shown in Table 11 and Figure 5b. Closer to a 70% accuracy score also came with 2/5 features: A and N type. The F1 score is very close to standalone evaluation and the AUC metric. Overall, the accuracy increases in the best case only by about a 4.8% rate \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Features** & **Model** & **Accuracy [\%]** & **Precision [\%]** & **Recall [\%]** & **Specificity [\%]** & **F1 Score** & **AUC** \\ \hline A, DtS & Ensemble & 62.4 & 54.2 & 61.4 & 59.2 & 0.58 & 0.61 \\ N, DtS & Gaussian SVM & 59.9 & 79.3 & 55.7 & 64.4 & 0.65 & 0.59 \\ NC, DtS & Gaussian SVM & 59.6 & 72.1 & 56.6 & 61.8 & 0.63 & 0.58 \\ C, DtS & Gaussian SVM & 57.9 & 56.4 & 60.1 & 59.2 & 0.58 & 0.63 \\ A, N & Weighted KNN & 59.1 & 81.0 & 56.2 & 66.3 & 0.66 & 0.60 \\ A, N, DtS & Gaussian SVM & 60.7 & 58.1 & 61.2 & 60.3 & 0.60 & 0.61 \\ A, NC & Quadratic SVM & 57.1 & 60.9 & 56.2 & 57.6 & 0.58 & 0.61 \\ A, NC, DtS & Ensemble & 61.8 & 58.1 & 59.4 & 59.2 & 0.59 & 0.60 \\ A, N, NC & Naive Bayes & 57.7 & 65.4 & 56.3 & 58.9 & 0.60 & 0.60 \\ A, N, NC, DtS & Ensemble & 63.2 & 59.2 & 61.3 & 60.8 & 0.60 & 0.62 \\ A, N, NC, C, DtS & Ensemble & 62.1 & 60.3 & 61.4 & 61.2 & 0.61 & 0.60 \\ \hline \hline \end{tabular}
\end{table}