## 6 Results and Discussions  
The primary focus of our developed supervised machine learning models is to predict a category or class from the input feature vectors. Feature vectors are known as n-dimensional vectors of numerical features that describe the characteristics of a sample object. Each row in the training dataset we developed before represents a feature vector with a length of 8, which is the number of our finally selected features. Classification-based detection algorithms normally have a categorical output like a \"disease\" or \"no disease\", \"1\" or \"0\", \"True\" or \"false\". We used 1 or 0 for categorization and put this label in the last column of our dataset. If a rescue patient is diagnosed with neurological diseases, the label is set to 1, and it is 0 if the patient is healthy or suffering from other health complications rather than neurological ones. We have considered (randomly) 23,090 rescue cases of non-neurological patients in the dataset to make the total dataset length of 45,872 feature vectors. The whole dataset has been split into two parts where one part is the training dataset and the other one is the testing dataset with a ratio of 70:30, respectively. The training dataset was fitted to each machine learning algorithm to build and train the model and teach how the expected output should look like. The purpose of testing the dataset is to evaluate the performance of each model with unknown feature vectors. We also performed three-fold cross-validation to check the model's performance by using a portion of training data. Cross-validation is a data resampling method to assess the generalization ability of predictive models and to prevent overfitting [41]. In this validation process, we trained the machine learning models with the subset of training data and then evaluate their performance with a grid of hyperparameters. The cross-validation technique applied here is K-fold cross-validation, where the random sampling is done in a way so that no two test sets overlap with each other. Performance of the selected algorithms in each fold of cross-validation can be seen from Table 3.  
Figure 6: Versatile Tensor Accelerator (VTA). To visualize the stability of the models' performance through cross-validation folds, a box and whisker plot can be used. Such a plot is also used to show how the data are distributed and if there are presence of outliers in the dataset. In Figure 7, the spread of the accuracy scores across each cross-validation fold for each algorithm is shown using a box and whisker plot. From the figure, it can be seen that the Naive Bayes model has a bigger box size, as can be also understood from its high standard deviation of 3.9% between folds' accuracy, but looking at the box shapes of other models, we can easily infer that the accuracy achieved through cross-validation folds is balanced and carries no outliers. A three-fold cross-validation technique was applied to the training dataset with 'GridSearch' and 'RandomSearch' methods to finalize the optimum hyperparameters of each ML model. After training the models with the selected hyperparameters, we started the evaluation process with the test dataset by plotting confusion matrices for each model. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Classification**} & **Fold** & **Fold** & **Fold** & **Mean CV** & **STD** \\  & **1** & **2** & **3** & **Score** & **Accuracy** \\ \hline XGB & 86.58 & 85.84 & 85.20 & 85.87 & 0.56 \\ \hline KNN & 84.85 & 84.03 & 83.26 & 84.05 & 0.65 \\ \hline Random Forest & 85.13 & 85.16 & 84.92 & 85.07 & 0.11 \\ \hline SVC & 86.08 & 85.17 & 84.69 & 85.31 & 0.58 \\ \hline Logistic Regression & 86.25 & 85.47 & 85.01 & 85.58 & 0.51 \\ \hline Naive Bayes & 81.09 & 81.57 & 73.07 & 78.58 & 3.90 \\ \hline ANN & 86.43 & 85.58 & 85.14 & 85.72 & 0.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Cross-Validation Performance of ML models. XGB showed the best performance in terms of cross-validation accuracy followed by the neural network model.