Mnist handwritten digit dataset. Unmasked features appear in black. The order in which the features were selected is indicated in [ blue]{}.[]{data-label="fig:mnist"}](im_a1.png "fig:"){width=".325\textwidth"} ![Demonstration of the proposed approach on the Mnist handwritten digit dataset. Unmasked features appear in black. The order in which the features were selected is indicated in [ blue]{}.[]{data-label="fig:mnist"}](im_a2.png "fig:"){width=".325\textwidth"} ![Demonstration of the proposed approach on the Mnist handwritten digit dataset. Unmasked features appear in black. The order in which the features were selected is indicated in [ blue]{}.[]{data-label="fig:mnist"}](im_a3.png "fig:"){width=".325\textwidth"} ![Demonstration of the proposed approach on the Mnist handwritten digit dataset. Unmasked features appear in black. The order in which the features were selected is indicated in [ blue]{}.[]{data-label="fig:mnist"}](im_a4.png "fig:"){width=".325\textwidth"} ![Demonstration of the proposed approach on the Mnist handwritten digit dataset. Unmasked features appear in black. The order in which the features were selected is indicated in [ blue]{}.[]{data-label="fig:mnist"}](im_a7.png "fig:"){width=".325\textwidth"} ![Demonstration of the proposed approach on the Mnist handwritten digit dataset. Unmasked features appear in black. The order in which the features were selected is indicated in [ blue]{}.[]{data-label="fig:mnist"}](im_a9.png "fig:"){width=".325\textwidth"}

Main Results
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Our goal is to obtain a good prediction for the outcome variable while considering a small number of features for each patient. As baselines, 