## 3 Results
### Types of Models Considered  
One of the most notable characteristics of the selected articles has to do with the variety of models used. Figure 3 shows a pie chart with the different models and the number of times they were considered in the articles. These models are: support vector machine (SVM), K-nearest neighbors (KNN), decision tree (DT), convolutional neural network (CNN), multilayer perceptron (MLP), random forest (RF), recurrent neural network (RNN), discriminant function analysis (DFA), fuzzy K-nearest neighbors (FKNN), naive Bayes (NB) and probabilistic neural network (PNN). It can be noticed from Table 3, and more specifically from Figure 3, that the number of models used exceeds the number of articles. This follows, as a consequence of the fact that four of the selected articles compared the results offered by different models, whereas five of them considered a single model. Concerning the type of processing associated to the models, both DT and RF belong to the group of symbolic models, whereas the remaining ones are subsymbolic. Hence, despite the diversity of the ML models considered, those whose processing was subsymbolic predominated. As an individual technique, SVM was the mostly used one, and as it will be seen later, it provided the best results for classifying patients with PD vs. healthy controls. On the other hand, it is worth emphasizing the fact that artificial neural networks (ANN) also played an important role in the reviewed articles since these techniques were used six times through CNN, MLP, RNN and PNN. Note that one of the articles considered two different models associated with RNN, with LSTM and GRU layers, respectively, as shown in Table 3, although this has not been taken into account, neither in Figure 3, nor in the previous computation. Taking into account that the most used models were SVM and KNN and the most used metric was accuracy, a comparative study between both models was carried out through a forest plot. To perform this analysis, it is necessary that several studies use these two models, and only two articles satisfied this condition [29; 37], respectively. Figure 4 shows the meta-analysis with the results of the accuracy of both models, for the choice of standard mean difference (SMD) for the effect measure, inverse variance Hedges' adjusted g for the algorithm, and fixed models for the effect models considered. Figure 3: Pie chart of the network types usage. As pointed out before, this plot shows the (standardized) difference of the means of SVM and KNN data. Thus, it favors the results with smaller values of the accuracy. For instance, in the first article, the lowest accuracy value was obtained by KNN and in the second article by SVM. Since the difference between both models is greater in the first article, the confidence interval (CI) is far away from zero, whereas in the case of the second article, as the values of the means are close to each other, the CI is close to zero. From the information shown in Figure 4, the difference between the number of subjects considered in each of the studies and how much the meta-analysis is affected by this fact become particularly noticeable. It is particularly striking how the sample size influences both the CI and the weight in each case. Indeed, the larger the number of patients, the smaller the amplitude of the CI, and the greater the weight. Since none of the confidence intervals crosses the 'no effect line', the difference between the models' SVM and KNN is statistically significant in both studies. However, as the overall result, the meta-analysis shows that there is no statistically significant benefit of choosing one model over the other, since the diamond crosses the 'no effect line'. One needs to keep in mind that only two articles are being compared, and that the meta-analysis exhibits a great heterogeneity, which makes the represented data less conclusive. Hence, more studies considering both ML models simultaneously are needed to provide a more reliable objective conclusion.