Gradient boosting machine model predicts psychiatric complications after deep brain stimulation in Parkinson’s disease

Figure 1 Research flowchart.

Figure 2 Least absolute contraction and selection operator regression screening of independent variables. A: Coefficient path diagram; B: Cross-validation curve.

Figure 3 Forest plot of the results of multivariate analysis. PSQI: Pittsburgh Sleep Quality Index; H-FRAT: Family Relationship Health Assessment Scale; UPDRS III: Unified Parkinson’s Disease Rating Scale Part III.

Figure 4 The scores of important variables. H-FRAT: Family Relationship Health Assessment Scale; UPDRS III: Unified Parkinson’s Disease Rating Scale Part III.

Figure 5 Receiver operating characteristic curve. A: Modeling set; B: Test set.

Figure 6 Calibration curve. A: Modeling set; B: Test set. ROC: Receiver operating characteristic; Dxy: Somers’ Dxy rank correlation; D: Discrimination index; U: Unreliability index; Q: Quality index; Eavg: Average error; S:z: Z value of the z test; S:p: P value of the z test.

Figure 7 Application effect of the gradient boosting machine prediction model. A: Receiver operating characteristic; B: Calibration curve. ROC: Receiver operating characteristic; Dxy: Somers’ Dxy rank correlation; D: Discrimination index; U: Unreliability index; Q: Quality index; Eavg: Average error; S:z: Z value of the z test; S:p: P value of the z test.

Figure 8 Decision curve. GBM: Gradient boosting machine.