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.22738926 · W7212555042
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This repository provides R code for developing and temporally validating a machine learning-based diagnostic model for endometriosis using routine clinical indicators, including XGBoost algorithm implementation and performance assessment.

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This repository provides the R code and analytical workflow for developing a machine learning diagnostic model for endometriosis using routine clinical indicators. The study employed repeated nested cross-validation, feature-selection stability assessment, and benchmark comparisons across six candidate algorithms to create an XGBoost model. Internal and temporal validation were conducted to assess discrimination, calibration, and decision-curve analysis, with SHAP-based interpretation used for model explainability. Individual-level data are not publicly available due to privacy restrictions, though the fitted model object may be requested from the authors. This paper is centrally about endometriosis — specifically the development of a machine learning tool for its diagnosis based on 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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