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Development of Rmlnomogram: An R Package to Construct an Explainable Nomogram for Any Machine Learning Algorithms.

Herdiantri Sufriyana1, Emily Chia-Yu Su1,2,3

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Summary
This summary is machine-generated.

This study introduces an R package and web application for creating nomograms from any machine learning (ML) algorithm, enhancing model explainability and deployment in clinical settings.

Keywords:
NomogramR packagemachine learningmodel explainabilitythe Shapley additive explanationweb application

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Area of Science:

  • Computational statistics
  • Machine learning interpretability
  • Clinical decision support

Background:

  • Nomograms are traditionally limited to regression models.
  • Expanding nomogram applicability to diverse machine learning (ML) algorithms can accelerate clinical deployment.
  • Lack of generalizable tools hinders ML model integration in healthcare.

Purpose of the Study:

  • To develop a versatile R package and web application for constructing nomograms.
  • To enable nomogram creation for any ML algorithm, incorporating model explainability.
  • To facilitate broader adoption and understanding of ML models in clinical practice.

Main Methods:

  • A function was developed to transform ML prediction models into nomograms.
  • Input data requires all predictor value combinations, model outputs, and optional explainability values.
  • A user-friendly web application was created to complement the R package.

Main Results:

  • The R package and web application support the creation of 5 types of nomograms.
  • These nomograms accommodate categorical and numerical predictors with binary or continuous outcomes.
  • The system supports up to 15 predictors for certain nomogram types and up to 3,200 data combinations.

Conclusions:

  • The developed R package and web application successfully generate nomograms for any ML algorithm.
  • Model explainability is integrated into the nomogram construction process.
  • The tools enable the use of a reasonable number of predictors for nomogram creation.