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International Journal of Fertility and Sterility، جلد ۱۱، شماره ۳، صفحات ۱۸۴-۱۹۰
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عنوان انگلیسی Predicting Implantation Outcome of In Vitro Fertilization and Intracytoplasmic Sperm Injection Using Data Mining Techniques
چکیده انگلیسی مقاله Objective: The main purpose of this article is to choose the best predictive model for IVF/ICSI classification and to calculate the probability of IVF/ICSI success for each couple using Artificial intelligence. Also, we aimed to find the most effective factors for prediction of ART success in infertile couples. Materials and methods: In this cross-sectional study, the data of 486 patients are collected using census method. The IVF/ICSI dataset contains 29 variables along with the label of each patient that is either negative or positive. Mean accuracy and mean area under the receiver operating characteristic (ROC) curve are calculated for the classifiers. Sensitivity, specificity, positive and negative predictive values, and likelihood ratios of classifiers are employed as the performance indicators. The state-of-art classifiers which are candidate for this study include support vector machines (SVM), recursive partitioning (RPART), random forest (RF), adaptive boosting (Adaboost), and one-nearest neighbor (1NN). Results: Random forest and recursive partitioning outperform the other compared methods. The results revealed the areas under the ROC curve (AUC) as 84.23% and 82.05%, respectively. The importance of features was extracted from the output of recursive partitioning. The results demonstrate that the probability of pregnancy is low for women aged above 38. Conclusion: Random forest and recursive partitioning predict IVF/ICSI cases better than other decision makers. Elicited decision rules of recursive partitioning determine useful predictive features of IVF/ICSI. The age of woman, number of developed embryos, and serum E2 level on the day of hCG administration are the three best features (out of 20), respectively.
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نویسندگان مقاله | Pegah Hafiz


| Mohtaram Nematollahi


| Reza Boostani


| Bahia Namavar Jahromi


نشانی اینترنتی http://ijfs.ir/journal/article/abstract/4882
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