Objective

2

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

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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

2

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

2

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.