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Bayesian Penalized Regression in High-Dimensional Data Analysis: A Case Study on Raman Spectroscopy for Disease Diagnosis
  • Dana Naderi ,
  • Mohammad Mahdavi ,
  • Farzad Eskandari
Dana Naderi
Allameh Tabataba’i University, Allameh Tabataba’i University

Corresponding Author:[email protected]

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Mohammad Mahdavi
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Farzad Eskandari
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Abstract

This study explores the use of Bayesian penalized regression models in analyzing high-dimensional Raman spectroscopy data for disease detection, showcasing superior accuracy compared to traditional machine learning methods. The findings introduce a groundbreaking approach that revolutionizes disease diagnosis by leveraging Bayesian analysis and shrinkage priors, enabling more precise and effective identification of infections in saliva or blood serum samples.