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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.05772 |
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| _version_ | 1866915097105924096 |
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| author | Sufriyana, Herdiantri Su, Emily Chia-Yu |
| author_facet | Sufriyana, Herdiantri Su, Emily Chia-Yu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_05772 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | rmlnomogram: An R package to construct an explainable nomogram for any machine learning algorithms Sufriyana, Herdiantri Su, Emily Chia-Yu Machine Learning I.2.6; I.5.m; I.2.4 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. |
| title | rmlnomogram: An R package to construct an explainable nomogram for any machine learning algorithms |
| topic | Machine Learning I.2.6; I.5.m; I.2.4 |
| url | https://arxiv.org/abs/2501.05772 |