Probability density and information entropy of machine learning derived intracranial pressure predictions
Fig 5
(A-D) A comparison of the empirical cumulative distribution function of intracranial pressure predictions derived from the Extreme Gradient Boost algorithm from four cases with contrasting two-sample Kolmogorov-Smirnov statistics from four cases. Two hold-out test cases demonstrating the lowest two-sample Kolmogorov-Smirnov statistic (tsKS). Cases 4 and 5 (A, B) demonstrate favorable concordance between venous and arterial derived predictions in contrast to the subset cases 8 and 9 (C, D), where the concordance is poor. The difference in separation of the ECDF between the two models can be observed in cases 8 and 9 (C, D). The tsKS statistic depends on a ratio parameter consisting of the product of the distribution data points divided by the sum [25]. Red = arterial model, Blue = venous model.