NON-INVASIVE PRENATAL DETECTION MODEL FOR FEMALE FETAL CHROMOSOMAL ANEUPLOIDY BASED ON XGBOOST
Keywords:
Non-Invasive prenatal testing, Chromosomal aneuploidy, XGBoost, Feature fusion, Female fetal detectionAbstract
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.References
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