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Table 2 Multivariate analysis results of the selected features for SET pregnancy, DET pregnancy and twin risk prediction

From: Individualized embryo selection strategy developed by stacking machine learning model for better in vitro fertilization outcomes: an application study

Selected features

P value

SET

DET

Pregnancy

Pregnancy

Twin Risk

Age

0.0222*

< 0.0001*

< 0.0001*

Attempt times of IVF

< 0.001*

< 0.0001*

< 0.0001*

Antral follicle count

0.3332

0.4016

0.4121

Follicle stimulating hormone

0.7307

0.8141

0.4633

Luteinizing hormone

0.3501

0.7230

0.4616

E2 on HCG day

0.0053*

0.9040

0.7684

Endometrial Thickness

0.0046*

< 0.0001*

0.2080

MII

0.9455

0.9444

0.0546

2PN

0.1041

0.9068

0.1021

Oocyte Number

0.7510

0.8897

0.6324

2PN/MII

0.5038

0.7772

0.0148*

Stimulation Protocol

 Agonist Protocol

0.3692

0.2019

0.4961

Endometrial Type

 A

0.8531

0.5324

0.4914

 C

0.7138

0.0887

0.4614

 Secondary Infertility

0.0816

0.2445

0.6809

Fertilization Method

 ICSI

0.2365

0.5069

0.8492

Embryo Features

 Blastomere Number

0.5422

NUa

NU

 Fragment

0.1585

NU

NU

 Equality

0.5399

NU

NU

Embryo Scores

 P1 + P2

NU

< 0.001*

0.1344

 P1 × P2

NU

0.2040

0.007*

  1. *P < 0.05
  2. aNU means this feature was not used in the corresponding level