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

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.

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.

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.

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.

Not applicable.