Orthogonality-guided feature selection for energy landscape analysis of health transitions

Abstract

Energy landscape analysis (ELA) offers a robust framework for examining state transitions in high-dimensional data, such as neuronal activity or microbial dynamics. The structure of the transition network from ELA depends heavily on the selected features. This study introduces a feature selection method using a scoring metric that measures orthogonality among binary representations of ELA-identified states. We applied this method to Specific Health Checkup data from Toyama Prefecture, Japan, and confirmed its effectiveness. Our results show that selecting features with this approach allows the ELA-based network to better capture major transitions in health status.