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This analysis proposes two predicting models for cars and trucks.
We found that the artificial neural network predicting model is the optimum predicting model from among three models.
We also compared the area under the receiver operating characteristic curves (aROCs) derived from Archimedes with those derived from two other diabetes predicting models, namely, the SAHS predicting model (2) and the Atherosclerosis Risk in Communities (ARIC) predicting model (3).
We also estimated the risk of diabetes for the same 100 individuals using both the SAHS diabetes predicting model and the ARIC predicting model.
A predicting model with the difficult colonoscopy score (DCS) was developed.
Increasing the case numbers and variables may increase the predicting model AUC.
From these, the model with minimum Modeller energy was chosen as a predicted model.
Although the predicted model fits the epidemic trend well, producing accurate predictions remains a challenge.
The positive predictive value (PPV) was calculated to assess the quality of predicted models.
(lower right) Characteristic quantities of the predicted model.
The maximum uranium recovery from the predicted models was 92.01%.
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CEO of Professional Science Editing for Scientists @ prosciediting.com