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.

中文翻译：

---

基于机器学习评价中医临床指标及脉搏波参数在多囊卵巢综合征诊断中的应用价值

介绍

多囊卵巢综合症（PCOS）是女性常见的内分泌疾病，常常导致排卵性不孕。本研究旨在建立并验证 PCOS 的有效预测模型，并利用脉搏波参数和中医临床指标探讨月经不调患者与 PCOS 患者特征的相关性。

方法

2018年8月至2022年1月，月经不调的女性纳入本研究。根据诊断标准，符合纳入标准的受试者被分为 PCOS 组和非 PCOS 组。脉搏波参数和中医临床指标由两名医疗专业人员收集。数据清理后，使用交叉验证的递归特征消除（RFECV）进行特征选择。使用四个监督机器学习分类器来构建 PCOS 预测模型，包括额外树 (ET)、随机森林 (RF)、极限梯度提升 (XGB/XGBoost) 和支持向量机 (SVM)。基于最佳模型的 SHapley 加法解释 (SHAP) 值被可视化，以进行进一步的特征解释。

结果

共有 450 名月经不调的女性参与了这项研究，其中 294 名患有 PCOS 的患者和 156 名没有 PCOS 的患者。基于RFECV，选取31个特征，包括12个脉搏参数和19个中医临床指标，建立预测模型。使用脉象和中医临床指标进行预测优于单独使用脉象参数或中医临床指标。SVM取得了最好的PCOS预测结果（准确度=0.837，AUC=0.878，F1分数=0.878）。对于脉搏参数，右As、右h4、左h1、左h3、左h5、左w/t和右t5较高值显示出明显的阳性PCOS预测作用。患有 PCOS 的女性更容易出现月经推迟、负面情绪、月经质地略呈果冻状、体重指数较高以及中医评估舌苔油腻等症状。

结论

建立了基于SVM算法的PCOS预测模型，并验证为区分月经不调的PCOS患者与非PCOS患者的最佳模型。利用脉搏波参数和中医临床指标的新预测模型为PCOS的诊断提供了一种无创且经济有效的方法，该模型为中医诊断提供了客观证据。