Medical Big Data-Driven Prenatal Risk Assessment and Testing-Time Optimization

Medical big data derived from clinical records, laboratory tests, sequencing outputs, and quality-control indicators provides new opportunities for individualized prenatal risk assessment and optimized screening strategies. This study proposes an interpretable computational framework for prenatal risk assessment and testing-time optimization by integrating ensemble learning, BMI-stratified analysis, and uncertainty evaluation. For male-fetus samples, Y-chromosome-related measurements were used as biologically meaningful proxies for fetal signal. Linear regression, polynomial regression, random forest regression, and least-squares boosting were evaluated using cross-validated root mean squared error and coefficient of determination. BMI-stratified monotonic success-rate functions across gestational age were then estimated using a sliding-window procedure to identify practical sampling windows. Monte Carlo perturbation and bootstrap resampling were further applied to assess robustness against measurement noise and threshold variation. Least-squares boosting achieved the best overall predictive performance. The estimated optimal sampling ages were approximately 10 weeks, 14 weeks + 5 days, and 23 weeks + 3 days for the low-, medium-, and high-BMI strata, respectively, with greater instability observed in the high-BMI stratum. For female-fetus samples, aneuploidy screening was formulated as a binary classification task. Random forest substantially outperformed logistic regression, with an ROC-AUC of 0.884 versus 0.538 and an average precision of 0.668 versus 0.070, and supported a decision threshold of 0.1437. These findings suggest that medical big data-driven methods can improve prenatal risk assessment, testing-time optimization, and uncertainty-aware decision support in prenatal screening.