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As shown in Table 2, the proposed framework achieved a testing accuracy of 0.81 with a sensitivity of 0.81 and a specificity of 0.80.
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It achieves a testing accuracy score of 99.75% to beat the rest of the architectures.
The system achieve a test accuracy of 37.1%, which is higher than the previous semantic parsing system and an information retrieval baseline.
Testing at a single visit boosts compliance while minimising consumption of resources to achieve a test result and the risk of loss to follow-up.
SCM has also been defined previously to include any mammary gland achieving a test result with a quarter somatic cell counts (QSCC) of >2 × 10 cells/ml.
According to Table 1, the performance of GKF1, GKF2 and LogE-KF including all 26 sequence-based features achieves a test accuracy of 86.68, 86.16 and 81.72%, respectively.
The aim will be to achieve a test duration of 8 to 12 minutes from commencement of the incremental phase of the test to peak exercise due to volitional task cessation or symptom limitation.
As stated above, the standard error of measurement (SEM) was set at 0.965, in order to achieve a test reliability of.94, as shown in the flowchart in Figure 1.
This threshold achieved a post test probability of <0.2 and therefore could be used to follow a watch and wait policy.
The 12 feature model obtained for the HS versus CS classification, using the z-transformation, achieved a blind test accuracy of 0.825 955% CI 0.727 0.909) and an AUC of 0.882, as shown in Figure 5.
The other 16 patients achieved a symptom-limited test below their predicted peak VO2, and also had additional abnormalities.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com