rmlnomogram: An R package to construct an explainable nomogram for any machine learning algorithms

TL;DR

This paper introduces rmlnomogram, an R package and web app that enables the creation of explainable nomograms for any machine learning model, facilitating deployment and interpretability in clinical settings.

Contribution

The authors developed a versatile R package and web application that can generate explainable nomograms for any ML algorithms, extending beyond traditional regression models.

Findings

Supports five types of nomograms for different predictor and outcome types.

Can handle up to 15 predictors and 3,200 combinations for most nomogram types.

Provides explainability values for types 2 to 5, enhancing model interpretability.

Abstract

Background: Current nomogram can only be created for regression algorithm. Providing nomogram for any machine learning (ML) algorithms may accelerate model deployment in clinical settings or improve model availability. We developed an R package and web application to construct nomogram with model explainability of any ML algorithms. Methods: We formulated a function to transform an ML prediction model into a nomogram, requiring datasets with: (1) all possible combinations of predictor values; (2) the corresponding outputs of the model; and (3) the corresponding explainability values for each predictor (optional). Web application was also created. Results: Our R package could create 5 types of nomograms for categorical predictors and binary outcome without probability (1), categorical predictors and binary outcome with probability (2) or continuous outcome (3), and categorical with single numerical predictors and binary outcome with probability (4) or continuous outcome (5). Respectively, the first and remaining types optimally allowed maximum 15 and 5 predictors with maximum 3,200 combinations. Web application is provided with such limits. The explainability values were possible for types 2 to 5. Conclusions: Our R package and web application could construct nomogram with model explainability of any ML algorithms using a fair number of predictors.