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

TL;DR

This paper introduces a hierarchical aggregation method for clustering-based predictors to improve the accuracy of predicting glaucomatous visual field loss, outperforming traditional methods on real datasets.

Contribution

It proposes a novel hierarchical aggregation approach that combines multiple cluster-based predictors, enhancing prediction accuracy for individual patients with limited data.

Findings

Hierarchical aggregation significantly improves prediction accuracy.

The method outperforms conventional clustering-based and regression methods.

Good predictors in small communities are effectively leveraged.

Abstract

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.