Machine learning methods with hierarchical data
General Linear Mixed Modeling (GLMM) has been an established method for classifying and predicting disease outcome in the field of Radiology. This paper provides a comparison of several machine learning methods to analyze hierarchically structured unbalanced dichotomous outcome data. The goal is to determine if the hierarchical structure of the described data makes a difference when choosing one of these methods. The methods assessed with GLMM were two-way Naïve Bayes (NB), Penalized Linear Discriminant Analysis (PDA), and Random Forests (RF). While all methods evaluated the dataset naïvely ...
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