A novel machine learning architecture to improve classification of intermediate cases in health: workflow and case study for public health.
Journal:
BMC bioinformatics
Published Date:
Jul 16, 2025
Abstract
BACKGROUND: The practice of medicine has evolved significantly during the past decade, with the emergence of Machine Learning (ML) that offers the opportunity of personalized patient-tailored care. However, ML models still face some challenges when classifying patients where clear-cut boundaries between classes are hard to identify. In this work, we propose an ML architecture to improve the sensitivity of detecting patients in intermediate "hard-to-classify" classes.
