## 4 Results
Moving to the results of the datasets when the SMOTE technique is applied, a slight decrease of overfitting for the dataset of the manually selected features and a significant increase of overfitting for the dataset with the automatically selected features are observed. Inspecting the datasets when the PCA method is applied, a significant underfitting for both datasets can be observed. Examining the results after the sequential application of both SMOTE and PCA, a better consistency of the accuracy for both datasets is observed ranging between 85.14% and 95.56% for the dataset with the manually selected features and between 89.78% and 97.78% for the dataset with the automatically selected features. Finally, only marginal variations in performance are observed when comparing these results to those that are achieved from the sequential application of SMOTE, PCA and HPO for the dataset with the manually selected features and in some cases for the dataset with the automatically selected features where the models either present overfitting (SVC, MLP) or underfitting (custom ensemble). Moving on to the results of the next stage of our methodology, the final models of this study are given which are built with the usage of pipelines to avoid any possible bias from data leakage. For these models, there is an interest to study their performance in terms of sensitivity and specificity as shown in Table 10. The first conclusion that can be drawn from this evaluation is that for both datasets there are models that score 100% on accuracy \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithm** & **Accuracy (\%)** & **Accuracy (\%)** & **SD** & **Sensitivity (\%)** & **SD** & **Specificity (\%)** & **SD** \\ \hline
**Manually Selected** & & & & & & & \\
**Feature Set** & & & & & & & \\ \hline Logistic Regression & 100 & 91.79 & 6.74 & 96.6 & 6.8 & 70 & 20 \\ \hline Decision Tree & 100 & 86.07 & 9.06 & 96.6 & 6.8 & 50 & 24.72 \\ \hline Random Forest & 98.79 & 88.93 & 10.62 & 96.6 & 6.8 & 70 & 20 \\ \hline Support Vector Classifier & 98.79 & 91.79 & 6.74 & 93.20 & 6.33 & 90 & 10 \\ \hline Gaussian Naive Bayes & 84.33 & 83.57 & 10 & 93.20 & 6.33 & 60 & 17.42 \\ \hline Multi-layer Perceptron & 100 & 94.64 & 6.59 & 96.60 & 6.8 & 90 & 10 \\ \hline k-Nearest neighbors & 98.79 & 89.29 & 9.58 & 90 & 13.25 & 90 & 10 \\ \hline
**Automatically Selected** & & & & & & & \\
**Feature Set** & & & & & & \\ \hline Logistic Regression & 96.38 & 89.64 & 8.66 & 89.4 & 6.62 & 90 & 10 \\ \hline Decision Tree & 100 & 86.79 & 6.72 & 90 & 6.52 & 80 & 14.49 \\ \hline Random Forest & 100 & 83.93 & 10.07 & 89.4 & 6.62 & 70 & 14.49 \\ \hline Support Vector Classifier & 96.38 & 89.64 & 8.66 & 89.4 & 6.62 & 90 & 10 \\ \hline Gaussian Naive Bayes & 98.79 & 92.14 & 8.2 & 93.4 & 6.2 & 90 & 10 \\ \hline Multi-layer Perceptron & 100 & 89.64 & 8.66 & 89.4 & 6.62 & 90 & 10 \\ \hline k-Nearest neighbors & 100 & 89.64 & 8.66 & 89.4 & 6.62 & 90 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Evaluation results, by ML algorithm, for the training and testing processes, for both feature selection strategies using the pipeline method. in training, so these models clearly overfit and they should be discarded. Hopefully, there are also models that do not overfit during the training, yet they do maintain relatively acceptable scores regarding the accuracy and the rest of the evaluation metrics. Taking into account the scores of sensitivity and specificity, we can distinguish as the best performing models those that are trained using the SVC and GNB algorithms. More specifically, the SVC based model yields an accuracy of 91.79% (6.74% SD), a sensitivity of 93.20% (6.33% SD) and a specificity of 90% (10% SD) for the dataset trained on the manually selected features, while the GNB based model yields an accuracy of 92.14% (8.2% SD), a sensitivity of 93.4% (6.2% SD) and a specificity of 90% (10% SD) for the dataset trained on the automatically selected features.