Research on Optimization of NIPT Detection Timing and Fetal Anomaly Determination Based on Multi factor Dynamic Modeling

Abstract

This article establishes a multi-stage mathematical model for the timing selection and fetal abnormality determination in NIPT testing, and comprehensively applies statistical analysis and machine learning methods to provide personalized testing strategies for different pregnant groups. Firstly, Spearman rank correlation analysis revealed a positive correlation (ρ=0.305) between Y chromosome concentration and gestational age, and a significant negative correlation (ρ=-0.534) with BMI. The conclusion was statistically significant after permutation test, Fisher z transform confidence interval estimation, and Benjamin Hochberg multiple test correction. Further validation through the Gradient Boosting Tree (GBDT) model revealed that the effects of proportion, gestational age, and BMI on Y concentration were most significant The model was cross validated with R²=01246. Although its generalization ability was limited, it provided a theoretical basis for subsequent modeling[1]. Based on K-means clustering, BMI was divided into three groups (low: 27.2-33.5, medium: 34.6-39.9, high: 41.0-44.2), and an optimization model was established with the goal of minimizing risk. Taking into account the early, middle, and late risk weights (1.0, 2.5, 5.0) and the re examination process, the optimal detection time points for each group were determined to be 11.0 weeks, 19.6 weeks, and 12.1 weeks, respectively, with an overall risk score of 119.80. By using the probability function and Monte Carlo simulation to analyze the detection error, the error tolerance of each group was determined to be±3.37 weeks,±4.02 weeks, and±3.75 weeks, respectively[2]. A follow-up strategy was proposed to control the error within±1.5 weeks, reducing the error by 55.5% -62.7%.