<p>Classification performance of all methods as a function of training set size, for a) first b) second, and c) third subjects.</p
<p>Accuracies are mean accuracies of test set performance over ten folds. (* 0.001</p
<p>Performance with training datasets of various sizes using gold standard medical concepts.</p
Performance of machine learning models on test set using the original imbalanced training set.</p
<p>Classification performance of all methods as a function of training set size, for a) first b) sec...
<p>Classification performance of all methods as a function of feature subset size, for a) first b) s...
<p>Classification performances of all methods as a function of feature subset size, for a) first b) ...
<p>Classification performances of the proposed method according to the number applied base classifie...
<p>The performances of the different classification algorithms as a function of the number of trials...
<p>Performance with training datasets of various sizes using automatically predicted medical concept...
<p>The performance of different classifiers associated with the attribute selection methods assessed...
<p>The classification performances of the 3 best classifiers for the 3 datasets.</p
<p>Performance comparisons of multiple individual classifiers on the training dataset by 10-fold cro...
<p>The performance comparison of the models trained with different sequence lengths.</p
Train and test accuracy for selected classifiers for different projection methods.</p
<p>Classification performance of the single metrics and multi-modal combinations.</p
<p>Accuracies are mean accuracies of test set performance over ten folds. (* 0.001</p
<p>Performance with training datasets of various sizes using gold standard medical concepts.</p
Performance of machine learning models on test set using the original imbalanced training set.</p
<p>Classification performance of all methods as a function of training set size, for a) first b) sec...
<p>Classification performance of all methods as a function of feature subset size, for a) first b) s...
<p>Classification performances of all methods as a function of feature subset size, for a) first b) ...
<p>Classification performances of the proposed method according to the number applied base classifie...
<p>The performances of the different classification algorithms as a function of the number of trials...
<p>Performance with training datasets of various sizes using automatically predicted medical concept...
<p>The performance of different classifiers associated with the attribute selection methods assessed...
<p>The classification performances of the 3 best classifiers for the 3 datasets.</p
<p>Performance comparisons of multiple individual classifiers on the training dataset by 10-fold cro...
<p>The performance comparison of the models trained with different sequence lengths.</p
Train and test accuracy for selected classifiers for different projection methods.</p
<p>Classification performance of the single metrics and multi-modal combinations.</p
<p>Accuracies are mean accuracies of test set performance over ten folds. (* 0.001</p
<p>Performance with training datasets of various sizes using gold standard medical concepts.</p
Performance of machine learning models on test set using the original imbalanced training set.</p
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