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Table 1 Summarizing the previous studies of predicting ART outcome

From: CNFE-SE: a novel approach combining complex network-based feature engineering and stacked ensemble to predict the success of intrauterine insemination and ranking the features

Research problem Dataset Features Analytical method Remarks
Predicting IVF outcomes 5275 records 67 different features Combination of Decision Tree and Genetic algorithm Low predictive accuracy with 73%
Patient-specific predictions of outcome after IUI 1438 patients who underwent 3375 IUI cycles 8 features Logistic regression analysis A few numbers of features
Predictive modeling of implantation outcome in IVF 3898 embryos 18 features Naive Bayes, Decision Tree, K Nearest Neighbors, SVM, multilayer perceptron, radial basis function network A small number of features
Determine the impact of sperm morphology on the success of IUI 412 couples with 530 IUI cycles 12 features statistical analysis A few samples studied
Outcome prediction of IUI based on sperm morphology and progressively motile sperm count 4251 first IUI cycles of 1166 couples 9 features multivariable logistic regression A few features considered
Predicting live birth after IVF complete cycle 113,873 women data Age and duration of infertility Logistic regression A few difference makers considered
Identifying and choosing the best sperms for ICSI 219 patients 13 features Naive Bayes, SVM, MLP, IBK, K-Star, Random Committee, J48, Random Forest Small set of patients
IVF outcome prediction relying on endometrial transcriptions 25 patients 20 feature PCA and HCA clustering Small number of patients
Predicting Implantation Outcome of IVF and ICSI the data of 486 patients 21 features SVM, Adaboost, RPART, RF, 1-NN A few features considered
Predicting the impact of homologous semen on the success rate of IUI 556 couples with 1401 IUI cycles 16 features Logistic regression Small dataset
Assessing the effects of FSH and clomiphene citrate on infertile women with unexplained infertility 2259 IUI cycles of 684 couples 6 features Logistic regression A few determinative factors studied
Outcome prediction of ART 257 infertile couples 12 features ANN Small dataset
Prediction of implantation after blastocyst transfer in IVF or ICSI 1052 patients in 32 features Random Forest, Multivariate logistic regression model A small number of features
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BMC Medical Informatics and Decision Making

ISSN: 1472-6947

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