A Robust Support Vector Machine Approach for Raman COVID-19 Data Classification

2025-01-29

AI Summary

To address the poor robustness of conventional SVMs in classifying Raman spectroscopic data for COVID-19 diagnosis—stemming from high biological noise and substantial inter-individual variability—this paper proposes the first robust optimization-based SVM framework tailored for Raman-spectrum-driven COVID-19 detection. Our method introduces a norm-bounded uncertainty set to uniformly model both linear and RBF-kernelized binary/multiclass SVMs, thereby providing explicit robustness guarantees against input perturbations. By integrating uncertainty set construction, robust optimization theory, and domain-specific Raman spectral feature analysis, the framework ensures stability under realistic measurement noise and physiological variation. Evaluated on real-world clinical Raman datasets collected across multiple Italian hospitals, our approach achieves over 5% higher accuracy than state-of-the-art classifiers, while demonstrating significantly improved noise resilience and cross-center generalization capability.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Responsible Web: Mitigating misinformation and disinformation
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

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.

Problem

• Raman COVID-19 data classification
• biological sample complexity
• traditional data analysis limitations

Innovation

• Robust Support Vector Machine
• Raman COVID-19 Data Classification
• Machine Learning and Optimization