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Similarly, in the case of the GPCRs dataset, the NP kernel led to the best predictive model, with RMSEPext and R 0 ext 2 values of 0.66 p K i units and 0.72.

A multivariate analysis demonstrated that UCH-L1 together with age and GCS results in the best predictive model, with a classification accuracy of 94% in dead versus alive after six months.

Overall, the input variable temperature, expressed as maximum, average or minimum, consistently appears in the best predictive models, with one of the three temperature variables appearing in over 90% of the best performing models for any prediction attempted.

Results: The BME-LUR was the best predictive model (R = 0.653) with the lowest root mean-square error (RMSE ;7.06 ppb), followed by the LUR model (R = 0.466, RMSE = 8.747) and the BME kriging model (R = 0.414, RMSE = 9.164).

Multiple linear regression with auxiliary input data provided the best predictive model (R2: 0.825; RMSE: 0.541); multiple linear regression with pure PXRF elemental data provided a quality model (R2: 0.772; RMSE: 0.685); simple linear regression was ineffective as producing significant model predictions.

The best predictive model in terms of sensitivity and specificity was obtained with positivity at 3 out of 5 variables (sensitivity 61.4%, specificity 61.8%, and accuracy 62%).

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.

Stepwise selection with a p value of 0.1 for backward elimination was used to select the best predictive model.

We chose the combination of feature sets with the best performance in accuracy for the generation of our best predictive model.

We performed cross-validation to test the predictive ability of the different models and to find the best predictive model.