Electronic health records of modern health facilities, are increasingly complex and systematic assessment of these records is virtually impossible without special computationally intensive methods

Clinicians and other health professionals are not familiar with these methods, and this book is the first publication that systematically reviews such methods, particularly, for this audience

The book is written as a hand-hold presentation also accessible to non-mathematicians, and as a must-read publication for those new to the methods

The book includes step by step data analyses in SPSS, and can, therefore, also be used as a cookbook-like guide for those starting with the novel methodologies machine learning

Preface1 Introduction to Machine Learning Part Three 2 Evolutionary Operations 3 Multiple Treatments 4 Multiple Endpoints 5 Optimal Binning 6 Exact P-Values 7 Probit Regression 8 Over-dispersion 9 Random Effects 10 Weighted Least Squares 11 Multiple Response Sets 12 Complex Samples 13 Runs Tests 14 Decision Trees 15 Spectral Plots 16 Newton's Methods 17 Stochastic Processes, Stationary Markov Chains 18 Stochastic Processes, Absorbing Markov Chains 19 Conjoint Models 20 Machine Learning and Unsolved Questions Index