Our experiments showed that this new approach offers more accuracy compared to methods which use linear models while remaining computationally light enough for implemention in real-time.
A series of one-way sensitivity analyses on parameter values and structural assumptions have been undertaken to test the robustness of model results to changes in individual model parameters while remaining assumptions were held constant.
Some of these batches were used to optimize the model while the remaining was used to test the methodology.
The coefficient of determination showed R2 = 0.06.9% which meant that only 6.9% of the variation in the dependent variable is explained by the model, while the remaining 93.1% is left unexplained.
In the first method, the jackknife test is a CV technique in which one dataset sequence is used as a testing data (for validation of the generated model) while the remaining dataset sequences are used as the training data to develop a model.
The union of two subsets was used to train the model, while the remaining subset was used as the test set, to assess the classification performance.
Although not readily apparent, the relations EquivalentGenes and GeneProteinEncoding are relationship sets in the sense of ER model while the remaining two are entity sets.
Due to their self-renewal abilities, even a minute population of CSC can form tumor when isolated and injected into an appropriate mouse model, while the remaining cells fail to do so [ 4].
Accordingly, adding both echocardiographic variables as covariates improved the prediction of NYHA classes III or IV in HFrEF, derived from the multivariate regression model, while AUC remained virtually unchanged in HFpEF (Fig. 3a).
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