Article Details

Abstract

To improve the accuracy of non-invasive prenatal testing and reduce clinical risks for pregnant women with high body mass index, this paper conducts research based on measured NIPT data. After data cleaning such as GC content filtering and gestational week format conversion, it constructs a correlation model between Y chromosome concentration and gestational week/BMI using Pearson correlation analysis and multiple linear regression verified by F-test. It optimizes BMI grouping and determines the optimal testing timing combined with K-means clustering, Random Forest Regression, and error analysis. Results show that Y chromosome concentration in male fetus correlates positively with gestational week and negatively with BMI (the model is overall significant), and only maternal weight affects the time to reach the Y chromosome threshold. It categorizes subjects into three groups by BMI. Without error, the optimal timing falls within the high-risk period of 13 to 27 weeks. The results remain stable after controlling for error. It suggests that NIPT for pregnant women with high BMI requires stratified testing based on gestational week, BMI, and weight. Controlling error reduces risk. Future studies can optimize the model with multi-regional samples.

Keywords

pregnant woman with high BMI; non-invasive prenatal testing; Y chromosome concentration; optimal testing timing; K-means clustering; Random Forest Regression; testing error control

References

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About the authors

Cao Xian (2003-), male, Han ethnicity, from Hengyang, Hunan, is currently pursuing a bachelor's degree with a research focus on software engineering.