Binary Decision Diagrams and Composite Classifiers for Analysis of Imbalanced Medical Datasets

Amandeep Singh, Olga Minguett, Tiziana Margaria

Research output: Contribution to journalArticlepeer-review

Abstract

Imbalanced datasets pose significant challenges in the development of accurate and robust classification models. In this research, we propose an approach that uses Binary Decision Diagrams (BDDs) to conduct pre-checks and suggest appropriate resampling techniques for imbalanced medical datasets as the application domain where we apply this technology is medical data collections. BDDs provide an efficient representation of the decision boundaries, enabling interpretability and providing valuable insights. In our experiments, we evaluate the proposed approach on various real-world imbalanced medical datasets, including Cerebral-stroke dataset, Diabetes dataset and Sepsis dataset. Overall, our research contributes to the field of imbalanced medical dataset analysis by presenting a novel approach that uses BDDs and composite classifiers in a low-code/no-code environment. The results highlight the potential for our method to assist healthcare professionals in making informed decisions and improving patient outcomes in imbalanced medical datasets.

Original languageEnglish
Pages (from-to)1-21
Number of pages21
JournalElectronic Communications of the EASST
Volume82
DOIs
Publication statusPublished - 2022

Keywords

  • ADD-Lib
  • Binary Decision Diagrams
  • Low-code
  • classifiers
  • data balancing techniques
  • decision support systems
  • imbalanced datasets
  • sampling techniques

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