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Fig. 3 | EJNMMI Research

Fig. 3

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. 3

Statistical analyses. After identifying potential collinearity between the PET metrics, the whole dataset (visual binary interpretation and semi-quantitative PET metrics from 110 18F-DOPA PET/MRI) was split into training (70%) and test (30%) sets. Five machine learning classifiers (KNN, Log Regression-Log Reg, Support Vector Machine-SVM, Random Forest-RF and tree gradient boosting) were trained to predict the final diagnosis at 11 months of median follow-up (IPD or control) on the training set by using a nested k-fold cross-validation procedure. (Each model parameters are fine-tuned and cross-validated while optimizing the bias of over fitting.) The overall nested cross-validation procedure was repeated 100 times. The best model on average was applied on the test set to provide general unbiased accuracy. Finally, the contribution of each 18F-DOPA PET parameter (the visual and four semi-quantitative metrics) in the model predictions was deciphered

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