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Background <p>Early miscarriage (EM) remains a significant reproductive health challenge, with current serum hormone biomarkers (β-hCG, progesterone, estradiol) showing inconsistent predictive value across studies. To address these limitations, artificial intelligence (AI) and machine learning (ML) techniques are increasingly applied to integrate complex biomarker data and improve the early prediction of EM.</p> Methods <p>This retrospective cohort study analyzed 17,926 singleton pregnancies (1,918 EM vs. 16,008 live births) from Chongqing (2017–2023), China, to establish optimal hormonal predictors and timing for EM risk assessment. Serum hormone concentrations were longitudinally analyzed across distinct gestational weeks to characterize temporal variations. To assess the predictive efficacy of individual biomarkers and composite models, we conducted receiver operating characteristic (ROC) curve analyses. The optimal predictive biomarker and the earliest gestational time point for EM detection were determined through comparative evaluation of area under the ROC curve (AUC) values derived from univariate and multivariate logistic regression models. Finally, a systematic machine learning approach employing ten distinct algorithms was implemented to screen and validate the most robust prediction model.</p> Results <p>Longitudinal analysis revealed distinct gestational patterns: β-hCG and estradiol showed weekly increases through 9 weeks before stabilizing, while progesterone demonstrated a biphasic pattern (decline until 7 weeks, subsequent rise). EM cases exhibited significantly lower hormone levels across all gestational weeks. Receiver operating characteristic (ROC) analysis was conducted to assess the predictive performance of both univariate and multivariate models, which revealed a progressive increase in accuracy with advancing gestational age (AUC range: 0.67–0.99). Notably, week 4 progesterone alone showed predictive capacity (AUC = 0.67, cut-off = 17.75 pg/ml). Machine learning evaluation of 10 algorithms identified an artificial neural network (ANN) incorporating β-hCG, progesterone, estradiol, and maternal age as optimal, achieving superior prediction accuracy at week 4 (training AUC = 0.82; validation AUC = 0.81). This multivariate model enables EM risk stratification earlier than conventional hormonal assessments.</p> Conclusions <p>Our findings resolve key controversies regarding predictive thresholds and timing while demonstrating the clinical potential of machine learning integration for early pregnancy risk assessment. The established week 4 prediction window offers critical opportunities for timely intervention and patient counseling.</p>

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Predicting of early miscarriage based on machine learning: a retrospective cohort study in China

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月4日

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  • Jia Cai,
  • Jialin Qu,
  • Tingjia Chai,
  • Hua Zou,
  • Dachuan Zeng,
  • Shu Li,
  • Fanghong Liao,
  • Chunli Li,
  • Qian Li

摘要

Background

Early miscarriage (EM) remains a significant reproductive health challenge, with current serum hormone biomarkers (β-hCG, progesterone, estradiol) showing inconsistent predictive value across studies. To address these limitations, artificial intelligence (AI) and machine learning (ML) techniques are increasingly applied to integrate complex biomarker data and improve the early prediction of EM.

Methods

This retrospective cohort study analyzed 17,926 singleton pregnancies (1,918 EM vs. 16,008 live births) from Chongqing (2017–2023), China, to establish optimal hormonal predictors and timing for EM risk assessment. Serum hormone concentrations were longitudinally analyzed across distinct gestational weeks to characterize temporal variations. To assess the predictive efficacy of individual biomarkers and composite models, we conducted receiver operating characteristic (ROC) curve analyses. The optimal predictive biomarker and the earliest gestational time point for EM detection were determined through comparative evaluation of area under the ROC curve (AUC) values derived from univariate and multivariate logistic regression models. Finally, a systematic machine learning approach employing ten distinct algorithms was implemented to screen and validate the most robust prediction model.

Results

Longitudinal analysis revealed distinct gestational patterns: β-hCG and estradiol showed weekly increases through 9 weeks before stabilizing, while progesterone demonstrated a biphasic pattern (decline until 7 weeks, subsequent rise). EM cases exhibited significantly lower hormone levels across all gestational weeks. Receiver operating characteristic (ROC) analysis was conducted to assess the predictive performance of both univariate and multivariate models, which revealed a progressive increase in accuracy with advancing gestational age (AUC range: 0.67–0.99). Notably, week 4 progesterone alone showed predictive capacity (AUC = 0.67, cut-off = 17.75 pg/ml). Machine learning evaluation of 10 algorithms identified an artificial neural network (ANN) incorporating β-hCG, progesterone, estradiol, and maternal age as optimal, achieving superior prediction accuracy at week 4 (training AUC = 0.82; validation AUC = 0.81). This multivariate model enables EM risk stratification earlier than conventional hormonal assessments.

Conclusions

Our findings resolve key controversies regarding predictive thresholds and timing while demonstrating the clinical potential of machine learning integration for early pregnancy risk assessment. The established week 4 prediction window offers critical opportunities for timely intervention and patient counseling.