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Finally, based on the best feature combination, we developed one best predictive model set and applied it to predict the primary tumor sites.
Table 4 shows the performance of the best predictive model set using the combination of three features (Gene + gMutation + Chromosome) over each tumor site.
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Similarly, the best predictive model within the Pfaffl data set was a combined model of ANPEP and PSCA, which could correctly assign 86.2% of patients to indolent and aggressive groupings based on Gleason score.
Within the 2−ΔΔCT data set, the best predictive model was a combined gene expression model of ANPEP and ABL1.
The total number of peaks yielded the best predictive model and is used for optimization of parameters setting.
We chose the combination of feature sets with the best performance in accuracy for the generation of our best predictive model.
Further studies in different patient groups with different models are needed to determine the best predictive model.
Then, stepwise linear regression analysis was performed to identify the best predictive model using important parameters.
We performed cross-validation to test the predictive ability of the different models and to find the best predictive model.
The best predictive model developed in this study incorporates 5 items from the baseline WOMAC questionnaire, specifically questions regarding baseline function and stiffness, although an extensive set of known determinants were considered as potential predictors.
The best predictive model accounted for 34% of the variance (R = 0.34, see Table 4).
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CEO of Professional Science Editing for Scientists @ prosciediting.com