Cardiomyopathy Diagnosis Model from Endomyocardial Biopsy Specimens: Appropriate Feature Space and Class Boundary in Small Sample Size Data
Journal:
arXiv
Published Date:
Mar 14, 2025
Abstract
As the number of patients with heart failure increases, machine learning (ML)
has garnered attention in cardiomyopathy diagnosis, driven by the shortage of
pathologists. However, endomyocardial biopsy specimens are often small sample
size and require techniques such as feature extraction and dimensionality
reduction. This study aims to determine whether texture features are effective
for feature extraction in the pathological diagnosis of cardiomyopathy.
Furthermore, model designs that contribute toward improving generalization
performance are examined by applying feature selection (FS) and dimensional
compression (DC) to several ML models. The obtained results were verified by
visualizing the inter-class distribution differences and conducting statistical
hypothesis testing based on texture features. Additionally, they were evaluated
using predictive performance across different model designs with varying
combinations of FS and DC (applied or not) and decision boundaries. The
obtained results confirmed that texture features may be effective for the
pathological diagnosis of cardiomyopathy. Moreover, when the ratio of features
to the sample size is high, a multi-step process involving FS and DC improved
the generalization performance, with the linear kernel support vector machine
achieving the best results. This process was demonstrated to be potentially
effective for models with reduced complexity, regardless of whether the
decision boundaries were linear, curved, perpendicular, or parallel to the
axes. These findings are expected to facilitate the development of an effective
cardiomyopathy diagnostic model for its rapid adoption in medical practice.
