Predicting Miscarriage Risk Based on Periodontal and Hematological Parameters Using Artificial Neural Network and XGBoost Models.

Background

Miscarriage is a significant global health concern, affecting approximately 10-20% of recognized pregnancies and resulting in substantial physical and psychological consequences. Chronic inflammatory conditions such as periodontitis may contribute to pregnancy loss through systemic inflammatory and immune-mediated pathways. However, the combined predictive value of periodontal destruction and hematological inflammatory markers for miscarriage risk remains insufficiently understood. This study aimed to evaluate the potential of periodontal and hematological parameters for predicting miscarriage risk using artificial intelligence-based models.

Methods

A total of 82 participants (41 women who experienced miscarriage and 41 healthy pregnant controls) were included in this study. Fourteen clinical variables, including maternal age, periodontal indices, and hematological inflammatory markers, were analyzed. Artificial Neural Network (ANN) and eXtreme Gradient Boosting (XGBoost) models were developed to predict miscarriage risk. Model performance was assessed using the coefficient of determination (R2), mean squared error (MSE), root mean square error (RMSE), mean absolute error (MAE), and Willmott's Index of Agreement.

Results

Clinical attachment loss (CAL) was identified as a primary predictor of miscarriage risk within the study cohort. Both machine learning models exhibited promising predictive potential; however, the XGBoost model showed enhanced performance compared to the ANN. XGBoost achieved an R2 of 0.92088 and an MSE of 0.0235, indicating consistent predictive capabilities for this preliminary dataset.

Conclusions

The results highlight a significant relationship between oral health, systemic inflammation, and adverse pregnancy outcomes. The integration of periodontal and hematological biomarkers within machine learning frameworks provides a non-invasive, cost-effective, and preliminary proof-of-concept approach for miscarriage risk assessment. These findings support the development of personalized risk prediction strategies and may facilitate earlier clinical interventions aimed at improving maternal and fetal health outcomes.