Interpretable machine learning for endometriosis classification: a rule-based approach

Saeed Anbari Moghadam, Elham Akhondzadeh Noughabi, Shahideh Jahanian Sadatmahalleh
BMC medical informatics and decision making · 2026 · vol. 26(1) · doi:10.1186/s12911-026-03622-x · PMID:42271312 · PMC13479425 · W7164174230
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A CN2 rule induction algorithm analyzed clinical data to generate interpretable rules for endometriosis classification, achieving high accuracy and AUC for binary and multi-class predictions.

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This study developed an interpretable, rule-based machine learning approach to classify endometriosis using clinical data from 1,489 records with 52 variables, applying the CN2 rule induction algorithm to derive diagnostic rules across different disease stages. The resulting model achieved an AUC of 0.906 and accuracy of 0.803 for binary classification, with average AUC of 0.705 for multi-class staging, and highlighted factors such as pelvic pain, dysmenorrhea, dyspareunia, severe bleeding, tumor markers, age, and BMI. Clinical experts validated the derived rules, but the paper does not report additional external validation or detailed limitations beyond aiming to fill gaps in prior modeling work. This paper is centrally about endometriosis — it presents an interpretable rule-based model for endometriosis classification and staging.

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Abstract

BACKGROUND: Endometriosis is a chronic gynecological disease characterized by the growth of endometrial-like tissue outside the uterus, leading to pelvic pain, infertility, and other major health complications. Though some studies have tried to determine the factors that influence endometriosis and its clinical manifestations, the predictive modeling approaches have many limitations. Most of the models are based on complex algorithms that are not interpretable in a clinical setting, and therefore, practitioners cannot easily apply the findings. Moreover, much of the previous research often focuses on specific patient subsets or limited disease stages, leaving critical gaps in understanding the condition comprehensively. The purpose of this study was to address these challenges by using a rule-based model for analyzing various clinical data at each stage of endometriosis. The aim will be to develop interpretable and actionable diagnostic rules that will be able to empower health professionals in better understanding, diagnosis, and management of endometriosis while filling the existing gaps in knowledge within the field. METHOD: Clinical data were collected and preprocessed to deal with missing values, selection of relevant features, and standardization of variables. The CN2 rule induction algorithm was applied to 1,489 records with 52 clinical variables to generate interpretable classification rules linking clinical variables with the probability of endometriosis. The performance of the model was evaluated using several metrics: accuracy, F1-score precision, recall, and AUC. Derived rules were validated by clinical experts to ensure both statistical rigor and practical relevance. RESULT: The CN2 model achieved an AUC of 0.906 and a classification accuracy of 0.803 for binary classification. For multi-class classification of disease stages, the model demonstrated an average AUC of 0.705. Key findings were pelvic pain, dysmenorrhea, dyspareunia, severe bleeding, tumor markers, age, and BMI. The rules generated by the CN2 model provided interpretable insights, facilitating better understanding and management of endometriosis. CONCLUSION: The CN2 rule induction model has been effective in bridging the gaps in endometriosis research by providing interpretable and actionable diagnostic rules. Its integration into clinical practice may improve the precision of diagnosis, reduce diagnostic delays, and offer strategies for treatment, hence possibly improving patient outcomes. CLINICAL TRIAL NUMBER: Not applicable.
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Abstract

Background Endometriosis is a chronic gynecological disease characterized by the growth of endometrial-like tissue outside the uterus, leading to pelvic pain, infertility, and other major health complications. Though some studies have tried to determine the factors that influence endometriosis and its clinical manifestations, the predictive modeling approaches have many limitations. Most of the models are based on complex algorithms that are not interpretable in a clinical setting, and therefore, practitioners cannot easily apply the findings. Moreover, much of the previous research often focuses on specific patient subsets or limited disease stages, leaving critical gaps in understanding the condition comprehensively. The purpose of this study was to address these challenges by using a rule-based model for analyzing various clinical data at each stage of endometriosis. The aim will be to develop interpretable and actionable diagnostic rules that will be able to empower health professionals in better understanding, diagnosis, and management of endometriosis while filling the existing gaps in knowledge within the field.

Method

Clinical data were collected and preprocessed to deal with missing values, selection of relevant features, and standardization of variables. The CN2 rule induction algorithm was applied to 1,489 records with 52 clinical variables to generate interpretable classification rules linking clinical variables with the probability of endometriosis. The performance of the model was evaluated using several metrics: accuracy, F1-score precision, recall, and AUC. Derived rules were validated by clinical experts to ensure both statistical rigor and practical relevance.

Result

The CN2 model achieved an AUC of 0.906 and a classification accuracy of 0.803 for binary classification. For multi-class classification of disease stages, the model demonstrated an average AUC of 0.705. Key findings were pelvic pain, dysmenorrhea, dyspareunia, severe bleeding, tumor markers, age, and BMI. The rules generated by the CN2 model provided interpretable insights, facilitating better understanding and management of endometriosis.

Conclusion

The CN2 rule induction model has been effective in bridging the gaps in endometriosis research by providing interpretable and actionable diagnostic rules. Its integration into clinical practice may improve the precision of diagnosis, reduce diagnostic delays, and offer strategies for treatment, hence possibly improving patient outcomes. Clinical trial number Not applicable. Similar content being viewed by others Abbreviations - IUD: - Intrauterine device - IUFD: - Intrauterine fetal death - EP: - Ectopic pregnancy - STD: - Sexually transmitted diseases - PID: - Pelvic inflammatory disease - BMI: - Body mass index - SD: - Standard deviation - ROC: - Receiver operating characteristic - CA: - Classification accuracy - F1: - F1 Score - SHAP: - SHapley Additive exPlanations - AUC: - Area under the curve - MCC: - Matthews correlation coefficient - KNN: - K-nearest neighbors

Acknowledgements

Not applicable. Funding The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author information Authors and Affiliations Corresponding author Ethics declarations Ethical approval and consent to participate The study protocol was approved by the Research Ethics Committee of Royan institute (code: IR.ACECR.ROYAN.REC.IR.ACECR.ROYAN.REC.1394.43). All procedures were in accordance with the ethical standards of the Regional Research Committee and with the Declaration of Helsinki 1964 and its later amendments. After explaining the study’s purposes, informed written consent and verbal assent were obtained from all participants. They were informed that their participation was voluntary, confidential and anonymous, and that they had the right to withdraw from the research at any time. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Rights and permissions Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. About this article Cite this article Anbari Moghadam, S., Akhondzadeh Noughabi, E. & Jahanian Sadatmahalleh, S. Interpretable machine learning for endometriosis classification: a rule-based approach. BMC Med Inform Decis Mak (2026). https://doi.org/10.1186/s12911-026-03622-x Received: Accepted: Published: DOI: https://doi.org/10.1186/s12911-026-03622-x

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MeSH descriptors

Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis

Citation neighborhood

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

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