# Diagnosing Schizophrenia Using Effective Connectivity of Resting-State EEG Data
## 3 Results and Discussion
These two lobes showed more difference between SCZ and HC even during mental activity [15]. [PERSON] et al. [25] showed that the most frequently selected features for classifying the SCZ vs. HC were from the frontal and occipital lobes. [PERSON] et al. [69] used the functional MRI (fMRI) scanning of SCZs and HCs while performing a task with three conditions of (1) self-reflection; (2) other reflection; and (3) semantic processing. They showed a connection between alteration in the right TPJ activity and the disorder in self/other differentiation, which could be associated with psychotic symptoms of schizophrenia and affect social functioning in these patients. Most of the selected features are at \(\theta\) and \(\beta\) frequency bands. An increase in the first episode and chronic SCZ patients in \(\theta\) frequency band is one of the most consistent observations in schizophrenia EEG/ERP studies, which can occur both locally and globally [70]. Furthermore, the EEG signals of SCZ patients show abnormal synchronization in \(\beta\) and \(\gamma\) bands, suggesting a crucial role in cognitive deficits and other symptoms of schizophrenia [71]. Table 3 shows the training and test performance for GNB, LDA, SVM, KNN, and RF classifiers that averaged over the five CN-CV outer folds. From Table 3, both the training and test scores are high, ensuring that overfitting has not occurred. Using the test dataset, the KNN classifier can discriminate SCZ from HC with the highest averaged total classification accuracy of 96.92%, followed by the RF, GNB, LDA, and SVM classifiers with total accuracies of 95.4%, 95.44%, 95.44%, and 94.67%, respectively. Comparing the sensitivity and specificity, GNB has the highest sensitivity of 96.67%, followed by RF, KNN, SVM, and LDA with sensitivities of 95.12%, 95%, 91.92%, and 88.59% while KNN has the highest specificity of 98.57%, followed by the SVM, LDA, RF, and GNB with the specificities of 97.14%, 97.14%, 95.71%, and 94.28%, respectively. With regard to precision, KNN has the highest value of 98.33%, followed by SVM, LDA, RF, and GNB with precision values of 96.92%, 96.33%, 95.48%, and 93.81%, respectively. Finally, KNN has the highest F1-score (F1 = 0.97), followed by RF and GNB (F1 = 0.95), SVM (F1 = 0.94), and LDA (F1 = 0.92) and the highest MCC (MCC = 0.94), followed by RF and GNB (MCC = 0.91), SVM (MCC = 0.90), and LDA (MCC = 0.86). These high classification accuracies across different classification algorithms prove that the selected features are highly discriminating between the two classes. For illustrative purposes, Figure 3 shows a scatter plot of the 62 SCZs (blue circles) and 70 HCs (black crosses), using the kernelized principal component analysis (KPCA) with the polynomial kernel [72]. From the figure, the SCZ and HC clusters are clearly separated, which supports the hypothesis of selecting highly discriminating features. It is worth noting that while the selected features were highly discriminating between the two classes, no correlation was found between the values of the selected features and the symptom severity or duration of illness in SCZ class that showed the selected features were closer to the HC class for patients with less severe symptoms or a shorter duration of illness. We then evaluated the performance of the classifiers by using the selected features in Table 2 from a new dataset available at RepOD [58]. Table 4 shows the performances of different classifiers discriminating 14 SCZs and 14 HCs from RepOD dataset using 5-fold CN-CV. From Table 4, the performance of all classifiers is above 90%, whereas the highest performance belongs to KNN, SVM, and RF classifiers with the sensitivity of 95.71%, specificity of 100%, precision of 100%, total accuracy of 97.86%, F1-score of 0.98 and MCC of 0.96. This performance is higher than the performance of studies [19; 20;