## 4 Discussion  
Because early treatment of PD enhances patients' quality of life, effective early screening methods are essential. Dopamine imaging and the assessment of alpha-synuclein levels in the blood or spinal fluid have been considered the standard screening tools, but these methods have limitations; for instance, dopamine imaging may lack cost competitiveness as a screening tool. Meanwhile, recent MRI studies have demonstrated that iron accumulation in the basal ganglia may serve as a potential biomarker of PD. In this study, we used QSM, a new MRI technique, to noninvasively measure iron accumulation in the brain and thus differentiate between patients with early PD and HCs. This technique demonstrated its ability to serve as a rapid machine learning-based screening tool in this patient population. Early PD diagnosis is challenging, as only subtle symptoms appear in the early stages [25]. The SVM model constructed in this study had an accuracy of 79% for identifying early PD, an accuracy rate similar to that demonstrated by experienced neurologists using clinical criteria (approximately 76%) [26]. Therefore, this machine learning-based technique, therefore, offers a diagnostic accuracy  
Figure 4: Performance of SVM and LR in classifying low and High Non-motor Symptoms Scale (NMSS) groups using backward elimination of features. The results are shown in regard to (**a**) accuracy, (**b**) area under the receiver operating curve (AUC), (**c**) sensitivity, and (**d**) specificity. The features were included as the training set from the region of interest with the highest F-score to the region of interest with the lowest F-score. Asterisk (*) shows significance (\(p<0.05\)). SVM = support vector machine. LR = logistic regression. rate comparable to that demonstrated by experienced neurologists but independent of the clinicians' involved skills. We found that QSM values were higher in patients with early PD than in HCs in all ROIs (GP, RN, DN, SNr, SNc, CN, and PUT). A previous study reported that iron accumulates in the basal ganglia (e.g., SNc) in patients with early PD and is progressively stored in nearby areas [6]. Our study also suggests that iron deposition in the deep grey matter may occur in early PD patients. In this study, we found that GP demonstrated the highest F-score for differentiating between patients with early PD and HCs. A previous study of QSM values demonstrated that iron accumulation in GP is prevalent in early PD [6], and another study found a positive correlation between GP iron content and PD severity [27]. However, data regarding these potential correlations are still limited; further research is needed to investigate the importance of GP iron content in patients with early PD. SVM has the best performance in discriminating early PD and HC when all features are used. The previous studies have demonstrated significant elevation of QSM values in basal ganglia regions such SNc, SNr, GP, or RN in PD, and the iron accumulation has a progressive pattern extending from SNc to adjacent basal ganglia regions as with the disease progression in patients with early PD [6; 28; 29]. In a perspective of classifying PD and HCs with SVM, this broad progressive pattern in the basal ganglia allows more information on constructing the classification model, resulting in better performance when using more features as shown in Figure 3.  
SVM showed higher performance than LR. SVM uses a kernel function to transform datasets into the required form of data. Because data of this study has a high dimension, SVM with the kernel function is superior to deciding a hyperplane to classify patients with early PD compared to LR. In a multivariate problem with data, SVM performance is better than LR [30]. Figure 3 also shows that the overall performance of SVM increases as the number of features increases, whereas LR does not. The kernel function in SVM helps solve the high dimensional non-linear classification problem [31].