Fig. 2: Training, validation, and test datasets. | npj Digital Medicine

Fig. 2: Training, validation, and test datasets.

From: A high-generalizability machine learning framework for predicting the progression of Alzheimer’s disease using limited data

Fig. 2

We extracted data for training, validation, and test from the longitudinal datasets ADNI1, ADNI-GO, and ADNI2. If a participant was diagnosed as MCI and both the MRI T1-weighted images and other non-image information used in our model were available, we selected the related data as a candidate for this study. We evaluated our model in the following two ways: first, as in previous studies, we tested the model trained based on the NA-ADNI dataset using repeated 10-fold cross-validation and test; in each iteration, 80% of the samples were used for training, 10% for validation, and 10% for test; second, to evaluate generalization across cohorts, we tested the model, which was trained based on the ADNI dataset using a totally “unknown” J-ADNI dataset.

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