TY - JOUR
T1 - Binary Decision Diagrams and Composite Classifiers for Analysis of Imbalanced Medical Datasets
AU - Singh, Amandeep
AU - Minguett, Olga
AU - Margaria, Tiziana
N1 - Publisher Copyright:
© (2022), (Universitatsbibliothek TU Berlin). All rights reserved.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - ADD-Lib
KW - Binary Decision Diagrams
KW - Low-code
KW - classifiers
KW - data balancing techniques
KW - decision support systems
KW - imbalanced datasets
KW - sampling techniques
UR - http://www.scopus.com/inward/record.url?scp=85195032142&partnerID=8YFLogxK
U2 - 10.14279/tuj.eceasst.82.1227
DO - 10.14279/tuj.eceasst.82.1227
M3 - Article
AN - SCOPUS:85195032142
SN - 1863-2122
VL - 82
SP - 1
EP - 21
JO - Electronic Communications of the EASST
JF - Electronic Communications of the EASST
ER -