## 4 Discussion
### Classification Performance  
Although various recent studies have utilized parts of the ABIDE I dataset in various machine learning paradigms, the discrepancy between the data employed makes the identification of the optimal methodological approach unclear. In this regard, comparison and/or evaluation of classification performance works cannot be definitely quantified due to the divergent information included in each study and it is beyond the scope of this paper. However, the nature of the employed features in similar methodological procedures enables an indicative comparison. As such, the framework that was employed in this work utilized a linear SVM classifier as the base for both classification and FS (applied as an internal procedure by the RFE-CBR) along with a 10-fold nested CV strategy and Bayesian optimization, resulting in 76.63% accuracy, 78.42% sensitivity, 74.27% specificity and 82.74% AUC. To the best of our knowledge, this is the highest performance achieved so far when incorporating the whole CC200 parcellation ABIDE I dataset with an SVM adaptation (Table 2). Of note is that several other studies have achieved high accuracy levels with other brain atlases (such as the AAL, HO, or Craddock CC400); however, our selection \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Classifier** & **Data** & **\#Total** & **\#Selected** & **Accuracy (\%)** \\  & & & **Features** & **Features** & **Features** \\ \hline
**Proposed Work** & **RFE-CBR/SVM** & **472 TD/399 ASD** & **473** & **136** & **76.63** \\ [PERSON] et al. (2020) [26] & RFE/SVM & 472 TD/399 ASD & 561 & 117 & 72.5 \\ [PERSON] et al. (2017) [24] & SVM & 530 TD 505 ASD & 19,900 & 600 & 65 \\ [PERSON] et al. (2019) [40] & SVM & 530 TD/505 ASD & 19,900 & 9950 & 68.3 \\ [PERSON] et al. (2020) [41] & SVM & 530 TD/505 ASD & - & - & 71 \\ [PERSON] et al. (2020) [42] & SVM & 401 TD/408 ASD & 19,905 & 19,902 & 69.3 \\ [PERSON] et al. (2020) [43] & Extra-Trees/SVM & 548 TD/506 ASD & 19,900 & 1935 & 72.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification results of previous studies utilizing the ABIDE I set and the CC200 brain parcellation scheme. The proposed study outperformed the previously reported results and is indicated in bold. Notation: #—number. of brain parcellation scheme was based on the fact the CC200 atlas is deemed to provide superior results, especially in whole-ABIDE/SVM applications [41; 43; 44]. In parallel, different machine learning methods have demonstrated exceptional performance (mostly with deep learning structures) [45]. Nevertheless, since our goal was to obtain information concerning important autism biomarkers, SVM models are considered ideal for comprehensive interpretation, effectively assessing the relation between class labels and features. On the contrary, deep learning approaches (such as artificial neural networks) diffuse the feature-contained information in a manner that is extremely difficult to define, sometimes missing the global picture [46]. Another important aspect to be taken into consideration is the number of data integrated in the machine learning initial set. By definition, excessive exclusion criteria can not only provide a homogenous group for subsequent classification processes, but also contribute to high performance leading to the effectiveness of the methods employed [47]. Howbeit, utilizing a small portion of the overall samples could disregard the inhomogeneous traits of autism-related attributes, thus overlooking the global aspects of ASD. On the other hand, inclusion of all subjects might have an impact on feature calculation (i.e., inaccurate FC due to high noise levels from head movement) [28]. As such, the dataset included in the proposed framework comprised of as much data as possible presuming that samples from distinct clinics and acquisition protocols could be utilized in the same training sample without issues (with exclusion criteria being individuals whose average movement was less than 0.2 mm during acquisition).