Figure 6 | Scientific Reports

Figure 6

From: Machine learning for comprehensive forecasting of Alzheimer’s Disease progression

Figure 6

The model accurately captures statistics of variables for which supervised methods do not. (A) The covariance values between the model (CRBM) and the random forests (RF) compared to the data. Theil-Sen estimators for the slope of the covariance values in the data relative to the CRBM and RF are shown. Large covariance values are off-scale and not shown on the figure. (B) The distribution of ADAS-Cog scores for the data, the model (CRBM), and the random forests (RF). The random forest models shown are the same models trained to predict single variables at single time points, shown in Fig. 5. The CRBM is conditioned on the baseline data and simulates the 18-month data, while the RF models predict the 18-month data from the baseline data. In both cases the CRBM accurately captures the statistics of the data, while the RF under-predicts the covariance values and the extent of the ADAS-Cog distribution. In both (A) and (B), the data from all 5 CV folds are shown together, and in (A) the Theil-Sen slope is computed on this combined dataset. The errors in the Theil-Sen slope and the error bars in (B) are standard deviations across the 5 CV folds.

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