Community-Based Hierarchical Positive-Unlabeled (PU) Model Fusion for Chronic Disease Prediction

TL;DR

This paper introduces PUtree, a hierarchical community-based positive-unlabeled learning model that improves chronic disease prediction by accounting for population differences and utilizing data augmentation and model fusion techniques.

Contribution

The paper presents a novel hierarchical PU learning algorithm that incorporates community-specific models, data augmentation, and adversarial risk estimation for enhanced disease prediction.

Findings

PUtree outperforms existing methods on benchmark datasets.

The approach effectively captures community differences in disease risk.

Model fusion improves classification robustness.

Abstract

Positive-Unlabeled (PU) Learning is a challenge presented by binary classification problems where there is an abundance of unlabeled data along with a small number of positive data instances, which can be used to address chronic disease screening problem. State-of-the-art PU learning methods have resulted in the development of various risk estimators, yet they neglect the differences among distinct populations. To address this issue, we present a novel Positive-Unlabeled Learning Tree (PUtree) algorithm. PUtree is designed to take into account communities such as different age or income brackets, in tasks of chronic disease prediction. We propose a novel approach for binary decision-making, which hierarchically builds community-based PU models and then aggregates their deliverables. Our method can explicate each PU model on the tree for the optimized non-leaf PU node splitting. Furthermore, a mask-recovery data augmentation strategy enables sufficient training of the model in individual communities. Additionally, the proposed approach includes an adversarial PU risk estimator to capture hierarchical PU-relationships, and a model fusion network that integrates data from each tree path, resulting in robust binary classification results. We demonstrate the superior performance of PUtree as well as its variants on two benchmarks and a new diabetes-prediction dataset.