Efficacy prediction of acupuncture and moxibustion combined with conventional treatment for PCOS-IR based on real-world data: machine learning and interpretability analysis

GUO Benjie; SUN Jiahao; YANG Yan; CHEN Zhuolin; LI Li; GUO Jing; SUN Jianhua; PEI Lixia

doi:10.13703/j.0255-2930.20250530-k0003

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Efficacy prediction of acupuncture and moxibustion combined with conventional treatment for PCOS-IR based on real-world data: machine learning and interpretability analysis

Acupuncture and Intelligent Medicine | 更新时间：2026-05-12

- Efficacy prediction of acupuncture and moxibustion combined with conventional treatment for PCOS-IR based on real-world data: machine learning and interpretability analysis
- Chinese Acupuncture & Moxibustion Vol. 46, Issue 5, Pages: 669-677(2026)
- 作者机构：
  
  南京中医药大学附属医院/江苏省中医院针灸康复科，南京　210029
  南京中医药大学针灸推拿学院
- 作者简介：
- 基金信息：
- DOI：10.13703/j.0255-2930.20250530-k0003
  CLC：
- Received：30 May 2025，
  
  Online First：02 February 2026，
  
  Published：12 May 2026
- 稿件说明：
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GUO Benjie, SUN Jiahao, YANG Yan, et al. Efficacy prediction of acupuncture and moxibustion combined with conventional treatment for PCOS-IR based on real-world data: machine learning and interpretability analysis[J]. Chinese Acupuncture & Moxibustion, 2026, 46(5): 669-677.
DOI：

GUO Benjie, SUN Jiahao, YANG Yan, et al. Efficacy prediction of acupuncture and moxibustion combined with conventional treatment for PCOS-IR based on real-world data: machine learning and interpretability analysis[J]. Chinese Acupuncture & Moxibustion, 2026, 46(5): 669-677. DOI： 10.13703/j.0255-2930.20250530-k0003.

摘要

目的：

基于机器学习构建针灸联合常规治疗干预多囊卵巢综合征合并胰岛素抵抗（PCOS-IR）疗效的预测模型。

方法：

收集两项真实世界研究的数据，分别用于训练集（284例）和验证集（132例），训练集为2023年1月至2024年9月就诊的PCOS-IR患者，验证集为2024年9月至2025年2月就诊的PCOS-IR患者。结合逻辑回归（LR）和随机森林（RF）算法进行预测特征选择，并基于筛选结果建立5种具有不同原理的代表性机器学习模型。通过受试者工作特征曲线（ROC）、校准曲线及决策曲线分析（DCA）评估最佳模型的预测效果，最后采用沙普利加性解释（SHAP）框架对最佳模型的预测结果进行解释。

结果：

用于模型构建的预测特征包括空腹胰岛素（FINS）、总胆固醇（TC）、月经周期最长时限（UML）、体质量指数（BMI）和丙氨酸氨基转移酶（ALT）。RF模型在准确度、精密度、F1分数和曲线下面积（AUC）方面表现最为均衡，表明该模型在预测PCOS-IR疗效方面具有优异的综合性能。SHAP分析进一步揭示了5个预测特征在疗效预测中的重要性，特别是FINS作为胰岛素水平的指标，在疗效预测中贡献最为突出。

结论：

构建的针灸联合常规治疗干预PCOS-IR疗效的预测模型，为识别获益人群、优化治疗策略提供了重要的实证依据。

Abstract

Objective

To construct an efficacy prediction model for polycystic ovary syndrome with insulin resistance (PCOS-IR) treated with acupuncture and moxibustion combined with conventional treatment based on machine learning.

Methods

Data from two real-world studies were collected for the training set (284 cases) and validation set (132 cases). The training set included the data of PCOS-IR patients visited from January 2023 to September 2024

and the validation set was composed of the patients visited from September 2024 to February 2025. Logistic regression (LR) and random forest (RF) algorithms were combined for predictive feature selection

and 5 representative machine learning models with different principles were built based on the screening results. The predictive effectiveness of the best model was assessed by receiver operating characteristic (ROC) curve

calibration curve

and decision curve analysis (DCA). Finally

the predictive results of the best model were interpreted using the Shapley additive interpretation (SHAP) framework.

Results

The predictive features used for model construction included fasting insulin (FINS)

total cholesterol (TC)

the upper limit of the menstrual cycle length cycle (UML)

body mass index (BMI)

and alanine aminotransferase (ALT). The RF model showed the most balanced performance in terms of accuracy

precision

F1 score

and area under curve (AUC)

suggesting that it achieved the overall favorable performance in predicting the efficacy on PCOS-IR. The SHAP analysis further revealed the importance of these 5 predictive features in efficacy prediction; and in particular

FINS

as an indicator of insulin level

was conductive most significantly to the efficacy prediction.

Conclusion

The efficacy prediction model constructed for PCOS-IR treated with acupuncture and moxibustion

combined with conventional regimens

provides an important empirical evidence for identifying beneficiary population and optimizing treatment strategy.

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