  In this paper, a mixed-effect modeling scheme is proposed to construct a
predictor for different features of cancer tumor. For this purpose, a set of
features is extracted from two groups of patients with the same type of cancer
but with two medical outcome: 1) survived and 2) passed away. The goal is to
build different models for the two groups, where in each group,
patient-specified behavior of individuals can be characterized. These models
are then used as predictors to forecast future state of patients with a given
history or initial state. To this end, a leave-on-out cross validation method
is used to measure the prediction accuracy of each patient-specified model.
Experiments show that compared to fixed-effect modeling (regression),
mixed-effect modeling has a superior performance on some of the extracted
features and similar or worse performance on the others.
