Fig. 2
From: Thalamocortical dysrhythmia detected by machine learning
Obtained model using support vector machine learning to differentiate between, respectively, tinnitus (N = 153) vs. controls (N = 264), pain (N = 78) vs. controls (N = 264), Parkinson disease (N = 31) vs. controls (N = 264), and depression (N = 15) vs. controls (N = 264). SVM learning can differentiate between the disorder and healthy control subjects with an accuracy between 75 and 94% in comparison to a random model. The true-positive rate (TPR) of the models and the area under the curve (ROC) were significantly higher for the obtained model in comparison to the random model, while the false-positive rate (FPR) was significantly lower. A significant difference was also identified by comparing the κ-statistic MAE and RMSE, confirming the strength of the tested model in comparison to the random model. (*indicates a significant effect p < 0.001). Black whiskers indicate standard errorsPlease check the edits to the sentence 'The true positive rate.........' in figure caption 2 is ok.Is correct
