Prediction of the Timing Selection of NIPT and Abnormality Determination of Fetus Based on Logistic Regression and Comprehensive Loss Function

© 2026 by the Authors. Licensee Whioce Publishing, Singapore. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )

Cite

XML

HTML

Abstract

Chromosomal abnormalities are categorized into chromosomal-level (ROH, polyploidy, aneuploidy), local copy number, and gene-level (insertion/deletion) types. Unlike invasive prenatal diagnostics with miscarriage risks, NIPT is non-invasive, reducing medical risks and maternal anxiety. This study addresses clinical NIPT bottlenecks (inaccurate timing, inconsistent abnormality determination) using high BMI pregnant women’s data via three core approaches: Spearman correlation and mixed-effects models confirm gestational age’s weak positive (rs = 0.084, p < 0.01) and BMI’s weak negative (rs = -0.155, p < 0.001) correlation with fetal Y chromosome concentration; BMI grouping + Logistic regression + comprehensive loss function identifies robust optimal detection timing for each group; K-means clustering (4 groups) + three-layer weighted risk model (accuracy 0.4, timeliness 0.4, stability 0.2) optimizes multi-factor timing. Rational timing and multivariate models improve detection accuracy, supporting early clinical decisions.

References

Xue Y, Ding J, He Q, et al., 2017, The Impact of Maternal Age, Gestational Age and Body Mass Index on the Proportion of Fetal Free DNA in Maternal Peripheral Blood. Chinese Journal of Prenatal Diagnosis (Electronic Edition), 9(3): 5–10.

Yang D, Zhang Y, Zhang J, 2008, Sensitivity Analysis of Schwedler-Type Single-Layer Reticulated Shells, Proceedings of the 12th Academic Conference on Spatial Structures, 34–38.

Wu N, 2022, Differentiation Research of Down’s Syndrome Serum Screening and NIPT in Pregnant Women of Different Ages, thesis, Anhui Medical University.

Yan S, Liu W, Yang P, et al., 2024, Multi-Population Sparrow Search Algorithm Based on K-Means Clustering. Journal of Beijing University of Aeronautics and Astronautics, 50(2): 508–518.

Liu X, Yu X, Huang Y, et al. Evaluation of Ecological Security Change Trend in Weinan City from 1985 to 2003 by Rank Correlation Coefficient Method. Journal of Anhui Agricultural Sciences, 38(29): 16341–16342.

Wan L, Mao B, 2008, Batch Calculation of Spearman Rank Correlation Coefficient. Environmental Protection Science, 34(5): 53–55, 72.

Hua T, 2013, Research on K-Means Clustering Algorithm. Journal of Huangshan University, 15(5): 17–19.

Previous article in this issue

Next article in this issue