Machine Learning-Based Diagnosis of Adenomyosis Uteri Using Serum and Urine miRNA Profiles

Methods

Serum and urine samples were collected from 59 patients undergoing surgery for chronic pelvic pain at the Endometriosis Center, RWTH Aachen University Hospital. Among them, 7 had isolated adenomyosis, 34 had histologically confirmed endometriosis, and 18 served as negative controls. miRNA profiling was conducted via next-generation sequencing. A comprehensive feature selection pipeline—including variance filtering, univariate testing, mutual information, and recursive feature elimination—was used to reduce dimensionality.

Classification models (Logistic Regression, Random Forest, Support Vector Machine, and Decision Tree) were trained with cross-validation and evaluated using accuracy, precision, recall, F1 score, and ROC-AUC.