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Theoretic YR of echocardiography was calculated for each classification.
For each classification, every ecoregion contains about the same number of grid boxes.
For each classification algorithm, the inclusion of the GWPCA loadings data was found to significantly improve classification accuracy.
The machine learning algorithm Random Forest (RF) was used to reduce the number of variables used for each classification scenario by 25.5 % ± 2.7%.
For each classification, pain modulation, stabilization exercise, mobilization and training, agreement was 90%, 83%, 58%and89%9% (κ = 0.77, 0.67, 0.11 and 0.75), respectively.
Table 2 reports the results for each classification method.
Mortality according to AKI occurence was compared for each classification.
We trained a one-vs-all (OVA) classifier for each classification method and each feature representation.
We also report Cohen's Kappa statistic of interrater reliability here for each classification (Cohen 1960).
Examples are provided below for each classification, together with their impact on various stakeholders.
To increase the accuracy of the system, a forward selection process was applied for each classification.
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