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A new variable selection strategy, the one-class genetic algorithm (OGA), improved classification for COVID-19, endometriosis, and dengue detection. This chemometric approach enhances disease diagnosis and biomarker discovery.

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Area of Science:

  • Chemometrics
  • Machine Learning
  • Clinical Diagnostics

Background:

  • One-class classifier (OCC) models are essential for target class modeling and control.
  • Existing OCC models like DD-SIMCA and OCPLS require robust variable selection for optimal performance.

Purpose of the Study:

  • To integrate a novel one-class genetic algorithm (OGA) with DD-SIMCA and OCPLS models.
  • To evaluate the enhanced classification performance in clinical applications including COVID-19, endometriosis, and dengue.

Main Methods:

  • Implementation of DD-SIMCA with alpha = 0.05 and OCPLS with partial robust M-regression (PRM).
  • Association of these OCC models with the one-class genetic algorithm (OGA) for variable selection.
  • Application and comparison of OGA-DD-SIMCA and OGA-PRM-OCPLS models on clinical datasets.

Main Results:

  • The OGA significantly improved classification performance across all three clinical applications.
  • OGA-PRM-OCPLS achieved 100% sensitivity for COVID-19 and endometriosis.
  • OGA-DD-SIMCA demonstrated superior performance for dengue classification with 100% sensitivity.

Conclusions:

  • The OGA enhances OCC model utility for disease classification and biomarker identification.
  • This chemometric approach offers potential for developing rapid, low-cost, and minimally invasive diagnostic screening methods.