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    Ton J. Cleophas, Aeilko H. Zwinderman.
    Summary: Machine learning and big data is hot. It is, however, virtually unused in clinical trials. This is so, because randomization is applied to even out multiple variables. Modern medical computer files often involve hundreds of variables like genes and other laboratory values, and computationally intensive methods are required. This is the first publication of clinical trials that have been systematically analyzed with machine learning. In addition, all of the machine learning analyses were tested against traditional analyses. Step by step statistics for self-assessments are included. The authors conclude, that machine learning is often more informative, and provides better sensitivities of testing than traditional analytic methods do.

    Contents:
    Preface
    Traditional and Machine-Learning Methods for Efficacy Analysis
    Optimal-Scaling for Efficacy Analysis
    Ratio-Statistic for Efficacy Analysis
    Ratio-Statistic for Efficacy Analysis
    Complex-Samples for Efficacy Analysis
    Bayesian-Networks for Efficacy Analysis
    Evolutionary-Operations for Efficacy Analysis
    Automatic-Newton-Modeling for Efficacy Analysis
    High-Risk-Bins for Efficacy Analysis
    Balanced-Iterative-Reducing-Hierarchy for Efficacy Analysis
    Cluster-Analysis for Efficacy Analysis
    Multidimensional-Scaling for Efficacy Analysis
    Binary Decision-Trees for Efficacy Analysis
    Continuous Decision-Trees for Efficacy Analysis
    Automatic-Data-Mining for Efficacy Analysis
    Support-Vector-Machines for Efficacy Analysis
    Neural-Networks for Efficacy Analysis
    Ensembled-Accuracies for Efficacy Analysis
    Ensembled-Correlations for Efficacy Analysis
    Gamma-Distributions for Efficacy Analysis
    Validation with Big Data, a Big Issue
    Index.
    Digital Access Springer 2019