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Evaluation of Forensic Data Using Logistic Regression-Based Classification Methods and an R Shiny Implementation.

Giulia Biosa1, Diana Giurghita2, Eugenio Alladio3,4

  • 1Forensic Toxicology Laboratory, Department of Health Surveillance and Bioethics, Catholic University of the Sacred Heart, F. Policlinico Gemelli IRCCS, Rome, Italy.

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Summary
This summary is machine-generated.

This study introduces penalized logistic regression for forensic toxicology, enabling likelihood ratio (LR) calculations in classification tasks. An R Shiny tool is provided to help practitioners widely adopt these powerful statistical methods.

Keywords:
Cllrclassificationforensic sciencelikelihood ratiologistic regressionseparation

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Area of Science:

  • Forensic Toxicology
  • Statistical Modeling
  • Analytical Chemistry

Background:

  • Classification methods are crucial in forensic toxicology for interpreting complex biomarker data.
  • Existing methods may have limitations when dealing with data separation or complex assumptions.

Purpose of the Study:

  • To demonstrate the utility of penalized logistic regression for forensic toxicology classification.
  • To introduce a flexible statistical framework for calculating likelihood ratios (LRs).
  • To encourage wider adoption of advanced statistical methods in forensic practice.

Main Methods:

  • Application of penalized logistic regression for two-class classification problems.
  • Calculation of likelihood ratios (LRs) using the proposed framework.
  • Development of an R Shiny online tool for practical implementation.

Main Results:

  • Successful application of penalized logistic regression to alcohol biomarker data for classifying chronic alcohol drinkers.
  • Demonstration of the method's flexibility with non-multivariate normal data.
  • Provision of a user-friendly R Shiny tool for classification and LR inference.

Conclusions:

  • Penalized logistic regression offers a robust approach for forensic toxicology classification and LR calculation.
  • The developed R Shiny tool facilitates the practical application of these methods.
  • This work promotes the broader use of powerful statistical tools in forensic science.