Most of the models classified the same patients into the known groups (e.g. classifying a patient in the PSA control group as a PSA recurrence and a patient in the SYS case group as a systemic progression).
All published models classified the drugs in a binary manner with respect to PLD induction and used the accuracy to evaluate the performance of the model, which enables comparison of the models.
The designed model classified the CHF, the CAD patients and a normal control group with rates of 97.83%, 93.79% and 100%, accuracy, specificity and sensitivity, respectively.
The texture identifier model classifies the images based on Dunham classification, while the porosity analyzer model determines the percentage of each type of pore spaces in the image.
To construct the template library, the model classifies the structure into different motifs, such as helices, hairpin loops, internal/bulge loops, pseudoknots, and N-way junctions (N ≥ 3).
The model classifies the tar compounds in groups, according to their structure, and it is able to predict the experimental results with reasonable accuracy.
That is, whilst the Sydney model classifies the second non-finite verb as the main verb and, therefore, as the process, as in (58a), the CG model instead chooses to annotate the aspectual verb as the process, as in (58b).
The rankings of each model were compared to understand whether each model classified the same patients in different risk strata.
Based on the model classifying the origins of hypoxia by Kingdom et al. (26), three principal classes are recognized: pre-placental, utero-placental and post-placental hypoxia.
