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ChatGPT-Enhanced ROC Analysis (CERA): A shiny web tool for finding optimal cutoff points in biomarker analysis.

Melih Agraz1,2, Christos Mantzoros3, George Em Karniadakis2,4

  • 1Department of Statistics, Giresun University, Giresun, Turkiye.

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This study introduces CERA, a user-friendly web tool for Receiver Operating Characteristic (ROC) curve analysis. CERA simplifies diagnostic test performance evaluation by integrating ChatGPT for output interpretation, making complex analyses more accessible.

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Area of Science:

  • Medical Diagnostics
  • Biostatistics
  • Health Informatics

Background:

  • Diagnostic tests are vital for disease identification.
  • Receiver Operating Characteristic (ROC) curve analysis is a key method for evaluating diagnostic test performance.
  • Determining optimal cut-off points in ROC analysis is critical but often complex.

Purpose of the Study:

  • To develop a user-friendly tool for ROC curve analysis.
  • To simplify the process of determining optimal cut-off points for diagnostic tests.
  • To enhance the accessibility and understanding of ROC analysis results for users without coding expertise.

Main Methods:

  • Development of a web tool named CERA (ChatGPT-Enhanced ROC Analysis) utilizing the Shiny interface.
  • Integration of ChatGPT for interpreting ROC analysis outputs.
  • Generation of interpreted reports using R-Markdown.

Main Results:

  • CERA provides a user-friendly interface for faster and more effective ROC curve analyses.
  • The tool simplifies data preprocessing and analysis, reducing the need for coding skills.
  • ChatGPT integration enhances the interpretation and understanding of analysis results.

Conclusions:

  • CERA offers a significant improvement in the accessibility of ROC curve analysis.
  • The tool empowers researchers and clinicians without extensive coding experience to perform and interpret diagnostic test evaluations.
  • CERA facilitates quicker and more informed decision-making based on diagnostic test performance metrics.