Author(s)

RuiYing Chen

Affiliation(s)

School of Computer Science and Artificial Intelligence, Lanzhou University of Technology, Lanzhou 730050, Gansu, China.

Corresponding Author

RuiYing Chen

ABSTRACT

Addressing the challenge of limited accuracy in non-invasive prenatal testing (NIPT) for female fetal chromosomal aneuploidy due to the absence of Y chromosome reference, this study innovatively proposes a multi-feature fusion detection model based on XGBoost. The model's innovations are threefold: first, it constructs a three-dimensional feature system integrating "Z-score-GC content-clinical indicators", breaking through the limitation of traditional methods relying on single chromosomal indicators; second, it leverages XGBoost's powerful capability in capturing nonlinear relationships to deeply explore complex interaction effects among multi-chromosomal features; third, through feature importance ranking, it systematically reveals for the first time the critical roles of GC content in chromosome 13 and Z-scores of chromosomes 18 and X in female fetal abnormality detection. Experimental results demonstrate that the model achieves an accuracy of 75.45%, precision of 75.63%, recall of 75.45%, and F1-score of 75.47%, significantly outperforming traditional methods. This study provides a novel technical approach for detecting female fetal chromosomal aneuploidy with substantial clinical application value.

KEYWORDS

Non-Invasive prenatal testing; Chromosomal aneuploidy; XGBoost; Feature fusion; Female fetal detection

CITE THIS PAPER

RuiYing Chen. Non-invasive prenatal detection model for female fetal chromosomal aneuploidy based on XGBoost. Journal of Pharmaceutical and Medical Research. 2025, 7(3): 38-43. DOI: https://doi.org/10.61784/jpmr3054.

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