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Computational prediction of implantation outcome after embryo transfer.
- Source :
- Health Informatics Journal; Sep2020, Vol. 26 Issue 3, p1810-1826, 17p
- Publication Year :
- 2020
-
Abstract
- The aim of this study is to develop a computational prediction model for implantation outcome after an embryo transfer cycle. In this study, information of 500 patients and 1360 transferred embryos, including cleavage and blastocyst stages and fresh or frozen embryos, from April 2016 to February 2018, were collected. The dataset containing 82 attributes and a target label (indicating positive and negative implantation outcomes) was constructed. Six dominant machine learning approaches were examined based on their performance to predict embryo transfer outcomes. Also, feature selection procedures were used to identify effective predictive factors and recruited to determine the optimum number of features based on classifiers performance. The results revealed that random forest was the best classifier (accuracy = 90.40% and area under the curve = 93.74%) with optimum features based on a 10-fold cross-validation test. According to the Support Vector Machine-Feature Selection algorithm, the ideal numbers of features are 78. Follicle stimulating hormone/human menopausal gonadotropin dosage for ovarian stimulation was the most important predictive factor across all examined embryo transfer features. The proposed machine learning-based prediction model could predict embryo transfer outcome and implantation of embryos with high accuracy, before the start of an embryo transfer cycle. [ABSTRACT FROM AUTHOR]
- Subjects :
- ALGORITHMS
BLASTOCYST
BLASTULA
CHORIONIC gonadotropins
COMPARATIVE studies
COMPUTER simulation
EMBRYO transfer
FOLLICLE-stimulating hormone
MACHINE learning
RESEARCH methodology
RESEARCH funding
BIOINFORMATICS
PREDICTION models
FETAL development
TREATMENT effectiveness
ELECTRONIC health records
RANDOM forest algorithms
Subjects
Details
- Language :
- English
- ISSN :
- 14604582
- Volume :
- 26
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- Health Informatics Journal
- Publication Type :
- Academic Journal
- Accession number :
- 144846457
- Full Text :
- https://doi.org/10.1177/1460458219892138