# Diagnosing Schizophrenia Using Effective Connectivity of Resting-State EEG Data
## 4 Conclusions  
In this study, we used STE for the first time to develop an MLA to diagnose schizophrenia from resting-state EEG data. Using the relief algorithm, we found a set of 10 discriminating features that differentiated between SCZ and HC. We then first checked the classification performance by using 5-fold CN-CV on our dataset (Table 3) and then on a new dataset available at RepOD [58] (Table 4). From Table 3, the highest accuracy belonged to the KNN classifier (Sensitivity = 95%, specificity = 98.57%, precision = 98.33%, total accuracy = 96.92%, F1-score = 0.97, and MCC = 0.94) and from Table 4, the highest accuracy belonged to KNN, SVM, and RF classifiers (Sensitivity = 95.7%, specificity = 100%, precision = 100%, total accuracy = 97.86%, F1-score = 0.98, and MCC = 0.96). We note that the performances indicated in Tables 3 and 4 are higher than typical values obtained from previous studies (Table 1) with a much lower number of features, and less complexity compared to the studies using deep learning approaches. We argue that this performance improvement was due to the effectiveness of the STE method that was employed in the present study. Furthermore, the number of SCZ and HC subjects in this study is higher than most previous studies [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23], which can increase the probability of an accurate diagnosis of schizophrenia. Finally, the selected features are mostly from the EC of occipital, frontal, parietotemporal, and centro-temporal regions that are in accordance with other research studies related to SCZ. This supports the idea that the proposed MLA can identify features from the regions that are mainly affected by SCZ and that the STE effective connectivity extracted from resting-state EEGs could contribute towards a better understanding of the underlying pathophysiology of schizophrenic illnesses. While the number of subjects in this study was higher than in most previous studies, it is recommended that the proposed MLA be trained on a bigger dataset with a higher number of SCZ and HC subjects in the future to have a more reliable classification performance. This proposed MLA also has the potential to be used in differentiating between various neuropsychiatric disorders such as major depressive disorder (MDD), bipolar disorder, autism and schizophrenia, as well as predict the response to different treatments available for these diseases. Thus, further work is required to investigate disease-related alterations of EC between brain areas in neuropsychiatric disorders and conditions other than schizophrenia and also the ability to predict the response to different treatments. Conceptualization, M.R. ; methodology, M.R., C.C., K.M., A.M. and A.D.; software, C.C., K.M., A.M., and A.D.; validation, C.C., K.M., and M.R. ; formal analysis, C.C., K.M., and M.R. ; investigation, M.R. ; resources, M.R. ; data curation, M.R. ; writing--original draft preparation, M.R. and C.C. ; writing--review and editing, M.R. and C.C. ; visualization, M.R. ; supervision, M.R. ; project administration, M.R. ; funding acquisition, M.R. All authors have read and agreed to the published version of the manuscript. This work was supported by the New York Institute of Technology's Institutional Support for Research and Creativity (ISRC) Grants. **Institutional Review Board Statement:** Ethical review and approval were waived for this study, due to study of existing data where the information is recorded by the investigator in such a manner that the subjects cannot be identified, directly or through identifiers linked to the subjects.