Optimizing stability of heart disease prediction across imbalanced learning with interpretable Grow Network.

Journal: Computer methods and programs in biomedicine
PMID: 40147157

Abstract

BACKGROUND AND OBJECTIVES: Heart disease prediction models often face stability challenges when applied to public datasets due to significant class imbalances, unlike the more balanced benchmark datasets. These imbalances can adversely affect various stages of prediction, including feature selection, sampling, and modeling, leading to skewed performance, with one class often being favored over another.

Authors

  • Simon Bin Akter
    Martin Tuchman School of Management, New Jersey Institute of Technology, Newark, 07102, NJ, USA.
  • Sumya Akter
    Martin Tuchman School of Management, New Jersey Institute of Technology, Newark, NJ 07102, USA.
  • Rakibul Hasan
    Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh; Department of Computer Science and Engineering, Northern University Bangladesh, Dhaka, Bangladesh.
  • Md Mahadi Hasan
    Department of Computer Science and Engineering, Asian University of Bangladesh, Ashulia 1349, Bangladesh.
  • David Eisenberg
    Department of Information Systems, Ying Wu College of Computing, New Jersey Institute of Technology, Newark, NJ 07102, USA.
  • Riasat Azim
    Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh.
  • Jorge Fresneda Fernandez
    Martin Tuchman School of Management, New Jersey Institute of Technology, Newark, NJ 07102, USA.
  • Tanmoy Sarkar Pias
    Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA.

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