Building and Interpreting Risk Models from Imbalanced Clinical Data

As more clinical data becomes available for research, it is important to be able to build effective models and understand the predictions made from them. In this paper, we present a case study modeling melanoma risk using structured clinical records. Advanced modeling techniques are required as the data set is large, sparse, and imbalanced. We explore the use of logistic regression, decision tree, and random forest classifiers with various...