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Objective <p>Polycystic ovary syndrome (PCOS) is a leading cause of female infertility, and early pregnancy outcomes in affected women are affected by multiple interactive factors; however, no well-established predictive tools are available. This study aimed to develop and validate machine learning (ML) models for predicting early pregnancy in women with PCOS and compare the predictive performance of different ML algorithms.</p> Methods <p>This was a secondary analysis of a multicenter randomized controlled trial including 994 PCOS patients recruited from 21 research centers. Candidate predictors included reproductive history, ultrasound parameters, and serum hormone levels. Missing data were processed using multiple imputation. Variables were initially screened by LASSO regression, and final predictors were determined by univariate and multivariate logistic regression. Three ML models—random forest (RF), support vector machine (SVM), and decision tree (DT)—were constructed. The dataset was randomly split into training (70%) and validation (30%) cohorts. Model performance was evaluated using receiver operating characteristic (ROC) curves, area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and F1 score.</p> Results <p>LASSO regression and logistic regression analyses identified four key predictors: body mass index (BMI), body weight, endometrial thickness, and sex hormone-binding globulin (SHBG). In the validation cohort, the RF model showed the best overall performance, with an AUC of 0.56 (95% CI: 0.493–0.628), sensitivity of 65.1%, and specificity of 47.0%. All models exhibited moderate predictive efficacy with an AUC range of 0.505–0.56. The RF model yielded a PPV of 24.8%, NPV of 83.3%, accuracy of 50.8%, and F1 score of 0.360.</p> Conclusion <p>ML-based predictive models for early pregnancy in PCOS patients were successfully established. All models demonstrated moderate discriminative ability, with the RF model showing relatively superior performance. These models may serve as adjunctive tools for clinical decision-making, while the moderate predictive power underscores the multifactorial complexity of early pregnancy in PCOS.</p>

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Construction and validation of a machine learning model for predicting early pregnancy in patients with polycystic ovary syndrome: a retrospective cohort study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月7日

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  • Yuxiao Liu,
  • Hong Yu,
  • Jin Peng,
  • Jiahui Zhao,
  • Zhuwei Gao,
  • Jiannan Yu,
  • Xiaoke Wu

摘要

Objective

Polycystic ovary syndrome (PCOS) is a leading cause of female infertility, and early pregnancy outcomes in affected women are affected by multiple interactive factors; however, no well-established predictive tools are available. This study aimed to develop and validate machine learning (ML) models for predicting early pregnancy in women with PCOS and compare the predictive performance of different ML algorithms.

Methods

This was a secondary analysis of a multicenter randomized controlled trial including 994 PCOS patients recruited from 21 research centers. Candidate predictors included reproductive history, ultrasound parameters, and serum hormone levels. Missing data were processed using multiple imputation. Variables were initially screened by LASSO regression, and final predictors were determined by univariate and multivariate logistic regression. Three ML models—random forest (RF), support vector machine (SVM), and decision tree (DT)—were constructed. The dataset was randomly split into training (70%) and validation (30%) cohorts. Model performance was evaluated using receiver operating characteristic (ROC) curves, area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and F1 score.

Results

LASSO regression and logistic regression analyses identified four key predictors: body mass index (BMI), body weight, endometrial thickness, and sex hormone-binding globulin (SHBG). In the validation cohort, the RF model showed the best overall performance, with an AUC of 0.56 (95% CI: 0.493–0.628), sensitivity of 65.1%, and specificity of 47.0%. All models exhibited moderate predictive efficacy with an AUC range of 0.505–0.56. The RF model yielded a PPV of 24.8%, NPV of 83.3%, accuracy of 50.8%, and F1 score of 0.360.

Conclusion

ML-based predictive models for early pregnancy in PCOS patients were successfully established. All models demonstrated moderate discriminative ability, with the RF model showing relatively superior performance. These models may serve as adjunctive tools for clinical decision-making, while the moderate predictive power underscores the multifactorial complexity of early pregnancy in PCOS.