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Because Hb level is frequently measured during cancer treatment, Hb changes may be a potentially important variable in building cancer prognosis models.
Using a large number of predictor variables cannot guarantee the performance of prognosis models if these variables are correlated with one another [ 30, 31].
To evaluate the performance of the proposed prognosis models, a full data set is divided into two sets: training and testing sets.
In a series of studies [ 22, 23], Shen et al. developed prognosis models for predicting the HRQOL of cardiac patients following rehabilitation and they come to the conclusion that hostility affects HRQOL directly and indirectly through depression.
This threshold value was the one that consistently generated relatively small networks with reasonable classification Acc. We also obtained other prognosis models with higher Acc. values, but with relatively very large networks (more than 500 nodes).
However, the significant association between hypoalbuminemia and patient outcomes noted in this study despite adjustments for all these conditions again highlights the importance of this risk factor in patient prognosis (models 1 and 2 of Tables 4 and 5).
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The article describes the updated prognosis model and validation results.
The authors validated the prognosis model based on the original reference projects.
Moreover, a recursive algorithm for online updating the parameters of the failure prognosis model is developed.
In this work, a 3D failure prognosis model is developed for interface initiated failures of monolithic ceramic crowns.
A case study is examined to demonstrate the ability and potential applications of the newly proposed failure prognosis model.
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