Machine learning-based evaluation of application value of traditional Chinese medicine clinical index and pulse wave parameters in the diagnosis of polycystic ovary syndrome

Abstract

Introduction

Polycystic ovary syndrome (PCOS) is a prevalent endocrine disorder in women that often leads to ovulatory infertility. This study aims to establish and validate an effective predictive model for PCOS and to explore the correlation of features between patients with irregular menstruation and those with PCOS, using pulse wave parameters and traditional Chinese medicine (TCM) clinical indices.

Methods

From August 2018 to January 2022, women with irregular menstruation were enrolled in this study. Subjects who met the inclusion criteria were categorized into PCOS and non-PCOS groups based on diagnostic criteria. Pulse wave parameters and TCM clinical indices were collected by two medical professionals. After data cleaning, recursive feature elimination with cross-validation (RFECV) was used for feature selection. Four supervised machine learning classifiers were used to build PCOS prediction models, including Extra Trees (ET), Random Forest (RF), Extreme Gradient Boosting (XGB/XGBoost), and Support Vector Machine (SVM). The SHapley Additive exPlanation (SHAP) values based on the optimal model were visualized for further feature explanation.

Results

A total of 450 women with irregular periods were enrolled in the study, consisting of 294 patients with PCOS and 156 without PCOS. Based on RFECV, 31 features, including 12 pulse parameters and 19 TCM clinical indices, were selected for building prediction models. Using pulse and TCM clinical index was superior to using pulse parameters or TCM clinical index alone in prediction. SVM achieved the best PCOS prediction results (accuracy=0.837, AUC=0.878, F1 score=0.878). For pulse parameters, lower values of right As, right h4, left h1, left h3, left h5, left w/t, and higher right t5 showed an obvious positive PCOS predictive effect. Women with PCOS were more likely to experience delayed menstruation, negative emotions, a slightly jelly-like menstrual texture, higher BMI, and a TCM assessment of greasy tongue coating.

Conclusion

A PCOS prediction model based on the SVM algorithm was established and verified as the best model for distinguishing between patients with PCOS and patients without PCOS with irregular menstruation. The new prediction model that uses pulse wave parameters and TCM clinical indices offers a non-invasive and cost-effective way to diagnose PCOS, and the model provides objective evidence for TCM diagnosis.