A Robust Support Vector Machine Approach for Raman COVID-19 Data Classification
Journal:
arXiv
Published Date:
Jan 29, 2025
Abstract
Recent advances in healthcare technologies have led to the availability of
large amounts of biological samples across several techniques and applications.
In particular, in the last few years, Raman spectroscopy analysis of biological
samples has been successfully applied for early-stage diagnosis. However,
spectra' inherent complexity and variability make the manual analysis
challenging, even for domain experts. For the same reason, the use of
traditional Statistical and Machine Learning (ML) techniques could not
guarantee for accurate and reliable results. ML models, combined with robust
optimization techniques, offer the possibility to improve the classification
accuracy and enhance the resilience of predictive models. In this paper, we
investigate the performance of a novel robust formulation for Support Vector
Machine (SVM) in classifying COVID-19 samples obtained from Raman Spectroscopy.
Given the noisy and perturbed nature of biological samples, we protect the
classification process against uncertainty through the application of robust
optimization techniques. Specifically, we derive robust counterpart models of
deterministic formulations using bounded-by-norm uncertainty sets around each
observation. We explore the cases of both linear and kernel-induced classifiers
to address binary and multiclass classification tasks. The effectiveness of our
approach is validated on real-world COVID-19 datasets provided by Italian
hospitals by comparing the results of our simulations with a state-of-the-art
classifier.
