  This study addresses the issue of predicting the glaucomatous visual field
loss from patient disease datasets. Our goal is to accurately predict the
progress of the disease in individual patients. As very few measurements are
available for each patient, it is difficult to produce good predictors for
individuals. A recently proposed clustering-based method enhances the power of
prediction using patient data with similar spatiotemporal patterns. Each
patient is categorized into a cluster of patients, and a predictive model is
constructed using all of the data in the class. Predictions are highly
dependent on the quality of clustering, but it is difficult to identify the
best clustering method. Thus, we propose a method for aggregating cluster-based
predictors to obtain better prediction accuracy than from a single
cluster-based prediction. Further, the method shows very high performances by
hierarchically aggregating experts generated from several cluster-based
methods. We use real datasets to demonstrate that our method performs
significantly better than conventional clustering-based and patient-wise
regression methods, because the hierarchical aggregating strategy has a
mechanism whereby good predictors in a small community can thrive.
