Trans-Balance: Reducing demographic disparity for prediction models in the presence of class imbalance

Chuan Hong*, Molei Liu, Daniel M. Wojdyla, Jimmy Hickey, Michael Pencina, Ricardo Henao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Introduction: Risk prediction, including early disease detection, prevention, and intervention, is essential to precision medicine. However, systematic bias in risk estimation caused by heterogeneity across different demographic groups can lead to inappropriate or misinformed treatment decisions. In addition, low incidence (class-imbalance) outcomes negatively impact the classification performance of many standard learning algorithms which further exacerbates the racial disparity issues. Therefore, it is crucial to improve the performance of statistical and machine learning models in underrepresented populations in the presence of heavy class imbalance. Method: To address demographic disparity in the presence of class imbalance, we develop a novel framework, Trans-Balance, by leveraging recent advances in imbalance learning, transfer learning, and federated learning. We consider a practical setting where data from multiple sites are stored locally under privacy constraints. Results: We show that the proposed Trans-Balance framework improves upon existing approaches by explicitly accounting for heterogeneity across demographic subgroups and cohorts. We demonstrate the feasibility and validity of our methods through numerical experiments and a real application to a multi-cohort study with data from participants of four large, NIH-funded cohorts for stroke risk prediction. Conclusion: Our findings indicate that the Trans-Balance approach significantly improves predictive performance, especially in scenarios marked by severe class imbalance and demographic disparity. Given its versatility and effectiveness, Trans-Balance offers a valuable contribution to enhancing risk prediction in biomedical research and related fields.

Original languageEnglish (US)
Article number104532
JournalJournal of Biomedical Informatics
Volume149
DOIs
StatePublished - Jan 2024

Keywords

  • Class imbalance
  • Demographic disparity
  • Federated learning
  • Model fairness
  • Predictive modeling
  • Transfer learning

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications

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