## 4 Discussion
Applying QSM to an SVM model for screening patients with potential early PD has the advantage of simplicity. In a previous study, an SVM model demonstrated an accuracy of 87%, a sensitivity of 79%, and a specificity of 93% in differentiating between patients with early PD and HCs; however, multiple modalities were used, including resting-state functional MRI (i.e., the amplitude of low-frequency fluctuations, regional homogeneity, and regional functional connectivity strength) and brain structural images (i.e., volume characteristics from grey matter, white matter, and cerebrospinal fluid) [32]. This use of multiple modalities makes the technique difficult to apply in a clinical setting. The simplicity of using QSM, on the other hand, allows for this method to be easily incorporated in clinical fields. In a sub-analysis of patients with low or high NMSS scores, GP and RN were selected as the test features with the best performance. However, limited data exist regarding the potential correlation between NMSS score and QSM values in GP and SN [28], with one study demonstrating no significant correlation between NMSS score and QSM values [17]. Further studies are therefore needed regarding the potential association between iron accumulation and NMSS score. Despite the subtle difference in QSM values we observed for low and high NMSS score groups, the proposed SVM model demonstrated a relatively high accuracy of 79% in differentiating between groups. The nonlinear approach of SVM may have allowed for a relatively high classification power, even though each value did not demonstrate the statistical significance reported in previous research [17]. Because non-motor symptoms affect the quality of life, this simple screening method of differentiating between patients with low and high NMSS scores may be clinically meaningful. Although QSM and SVM have demonstrated usefulness for screening, QSM as a technique has some limitations, and this study was limited in some methodological aspects. The major limitation of QSM is the offset problem which occurs during the inversion process from k-space to field map [9]; offset compensation is required on QSM images. In this study, we constructed SVM models using a relatively small sample size. To address this limitation, 10-fold cross-validation was repeated in randomized order 10 times to calculate the models' average performance. Even though the models demonstrated high accuracy rates, further studies using larger sample sizes may be needed. The recent trend for machine learning-related classification is to use a deep learning approach. However, because of the small sample size in this study, a traditional SVM model may be more appropriate, as the deep learning approach requires thousands, millions, or even billions of samples [33]. In addition, it is difficult to eliminate the comorbidity factor in the SVM based QSM method. Thus, clinicians should consider clinical data, along with QSM results, in diagnosing or screening early PD patients. Finally, we manually drew ROIs in deep gray matter to obtain QSM values in this study; recent studies have reported using auto segmentation of deep gray matter areas on a QSM map [34; 35]. Adopting this approach with our suggested models may improve the practicality of this technique.