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The results reflect that the developed approach has better forecasting performance than other methods considered in this study.
The four NAH methods considered in this study are based on: (1) spatial Fourier transform, (2) equivalent sources model, (3) boundary element methods and (4) statistically optimized NAH.
For unbalanced data, our approach maintains the balance between sensitivity and specificity more adequately than many other classification methods considered in this study.
Table 10 shows the external predictive power of the models developed from different 3D conformations, as well as the average of the rankings corresponding to the conformer generation methods considered in this study.
A primary intent of choosing a buffer size of 400 m was to match a predefined spatial analysis unit, which was used to aggregate and produce the resulting outputs (i.e., estimates of crashes) from the two methods considered in this study.
Both methods considered in this study are computationally efficient.
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The method considered in this study is based on a Ray Launching method.
Our study shows that the proposed one outperforms other methods considered in this paper.
In addition, the average IOL power with the Norrby's ACD prediction was 19.44 ± 4.84D with a median of 21D, the lowest of all the methods considered in the study, including the ray tracing approach with the ACD prediction presented in this paper, as can be seen at Table 1.
For SIS, we followed the pre-selection and cross-validation procedure by [ 25] to be able to compare MU with the Bayesian genomic prediction methods considered in that study.
It is evident from the results reported in our previous study [ 26] that univariate LR models performed uniformly worse than multivariable models that utilized multiple biomarkers using any of the three methods considered in that study, namely, multivariable LR, PLR and CART.
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