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This study addresses the issue of predicting the glaucomatous visual fieldloss from patient disease datasets. Our goal is to accurately predict theprogress of the disease in individual patients. As very few measurements areavailable for each patient, it is difficult to produce good predictors forindividuals. A recently proposed clustering-based method enhances the power ofprediction using patient data with similar spatiotemporal patterns. Eachpatient is categorized into a cluster of patients, and a predictive model isconstructed using all of the data in the class. Predictions are highlydependent on the quality of clustering, but it is difficult to identify thebest clustering method. Thus, we propose a method for aggregating cluster-basedpredictors to obtain better prediction accuracy than from a singlecluster-based prediction. Further, the method shows very high performances byhierarchically aggregating experts generated from several cluster-basedmethods. We use real datasets to demonstrate that our method performssignificantly better than conventional clustering-based and patient-wiseregression methods, because the hierarchical aggregating strategy has amechanism whereby good predictors in a small community can thrive.

Predicting Glaucoma Visual Field Loss by Hierarchically Aggregating Clustering-based Predictors