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EJNMMI Research
Fig. 7 | EJNMMI Research

Fig. 7

From: Relevance of 18F-DOPA visual and semi-quantitative PET metrics for the diagnostic of Parkinson disease in clinical practice: a machine learning-based inference study

Fig. 7

Shap plot of features’ contribution on the best classifier model output. A Stacked bars of absolute value of the SHAP values for each feature sorted by their importance across all patients for classification. “Class 0” corresponds to the controls and “Class 1” to the IPD group. For classification of IPD versus control, PET visual score is the most contributive parameter, then min SUV ratio is the second most contributive parameter. The three others do not appear relevant for the classification of IPD versus controls here. B SHAP distribution on x-axis of the five features for every patient and control. The impact is correlate to the absolute value of the SHAP value. The higher (redder) feature value, the more it contributes to IPD classification. On the opposite the lower (bluer) feature value, the more it contributes to control classification. Min SUV ratio and intra-striatal gradient are inversely correlated to IPD classification as represent dopamine denervation at different levels

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