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The concept of comparing predictive models to models developed using random (or scrambled) data is common when developing and validating quantitative models such as QSAR equations [19].
A statistical model to predict systemic progression (with and without clinical variables) using a training set was developed using random forests [21] and logistic regression as described in Methods.
The model logic detailed in Figures 1 3 was implemented in FORTRAN, with agent properties developed using random number generators over a long set of simulations (here, 2000 time steps).
An unbiased labeling protocol was developed using random primers.
The final models were developed using Random forest algorithm implemented in WEKA package [ 27, 28].
The models developed using Random Forest can be visualized graphically by using multidimensional scaling to plot the relative similarity between patients in the trees [ 19].
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Linear and restricted cubic spline (RCS) models (with three knots, located at the 10th, 50th and 90th percentiles of the data) of the natural logarithm-transformed risk estimates were developed using random-effects meta-analysis methods, to incorporate heterogeneity.
The prediction model was developed using a random sample of 50%% of the dataset (development group), and was subsequently tested upon the remaining 50%% (validation group).
The k-NN models were developed using the random forest distance metric.
A statistical orthosis selection model was developed using the Random Forest Algorithm (RFA).
Then, separate crash risk analysis models were developed using Bayesian random parameter logistic regression technique; data from Shanghai urban expressway system were employed to conduct the empirical study.
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