R Code for Development and Temporal Validation of a Machine Learning–Based Diagnostic Model for Endometriosis Using Routine Clinical Indicators

Qun Hu, Hao Li, Shenao Ma, Ziming Zhao, Taotao Rong, Luyao Wang, Ji Wei, Guoliu Ye
2026 · doi:10.5281/zenodo.22738925 · W7213134908
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This repository provides R code for developing and temporally validating a machine learning diagnostic model for endometriosis using routine clinical indicators, including XGBoost implementation and SHAP-based interpretation.

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This repository provides the R code used to develop and temporally validate a machine learning diagnostic model for endometriosis using routine clinical indicators. The analytical workflow incorporates repeated nested cross-validation, feature-selection stability assessment, and comparisons of six candidate algorithms, ultimately selecting an XGBoost model for internal and temporal validation. The study also includes discrimination and calibration assessments, decision-curve analysis, and SHAP-based interpretation to ensure model robustness and interpretability. Individual-level clinical data are restricted due to privacy concerns, but the fitted model object may be available from the authors upon request. This paper is centrally about endometriosis — specifically the development of a machine learning diagnostic tool based on routine clinical indicators.

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Abstract

This repository contains the R code used for the formal model-development and validation analyses in the study entitled “Development and Temporal Validation of a Machine Learning–Based Diagnostic Model for Endometriosis Using Routine Clinical Indicators.” The analytical workflow includes repeated nested cross-validation, feature-selection stability assessment, comparison of six candidate algorithms, XGBoost model development, internal and temporal validation, discrimination and calibration assessment, decision-curve analysis, SHAP-based model interpretation, and benchmark comparisons. The repository also provides session information documenting the R environment and package versions used for the formal analysis. Individual-level clinical data are not publicly distributed because of privacy and ethical restrictions. The fitted final XGBoost model object may be available from the corresponding author upon reasonable request, subject to applicable institutional and data-governance requirements.
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R Code for Development and Temporal Validation of a Machine Learning–Based Diagnostic Model for Endometriosis Using Routine Clinical Indicators Authors/Creators - 1. Department of Gynecology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, China - 2. Graduate School, Bengbu Medical University, Bengbu, Anhui, China - 3. School of Clinical Medicine, Bengbu Medical University, Bengbu, Anhui, China Description This repository contains the R code used for the formal model-development and validation analyses in the study entitled “Development and Temporal Validation of a Machine Learning–Based Diagnostic Model for Endometriosis Using Routine Clinical Indicators.” The analytical workflow includes repeated nested cross-validation, feature-selection stability assessment, comparison of six candidate algorithms, XGBoost model development, internal and temporal validation, discrimination and calibration assessment, decision-curve analysis, SHAP-based model interpretation, and benchmark comparisons. The repository also provides session information documenting the R environment and package versions used for the formal analysis. Individual-level clinical data are not publicly distributed because of privacy and ethical restrictions. The fitted final XGBoost model object may be available from the corresponding author upon reasonable request, subject to applicable institutional and data-governance requirements. Files EM_code_repository_for_Zenodo.zip Files (14.0 kB) | Name | Size | Download all | |---|---|---| | md5:01c9881a8529b0e420251f48030901b7 | 14.0 kB | Preview Download | Additional details Software - Programming language - R

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