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CNFE-SE: A novel hybrid approach combining complex network-based feature engineering and stacked ensemble to predict the success of Intrauterine Insemination and ranking the features

Sima Ranjbari,Toktam Khatibi,A. V. Dizaj,Hesamoddin Sajadi,M. Totonchi,F. Ghaffari

Published 2020 in Unknown venue

ABSTRACT

Background: Intrauterine Insemination (IUI) outcome prediction is a challenging issue with which the assisted reproductive technology (ART) practitioners are dealing. Predicting the success or failure of IUI based on the couples' features can assist the physicians to make the appropriate decision for suggesting IUI to the couples or not and/or continuing the treatment or not for them. The large number of studies that have been focused on predicting the IVF and ICSI outcome by machine learning algorithms. But, to the best of our knowledge, a few studies have been focused on predicting the outcome of IUI. The main aim of this study is to develop an automatic classification and feature scoring method to predict intrauterine insemination (IUI) outcome and ranking of the most significant features, based on patients' cycle’s characteristics under IUI treatment.Methods: For this purpose, a novel hybrid approach combining complex network-based feature engineering and stacked ensemble (CNFE-SE) is proposed. Three complex networks are extracted considering the patients' data similarities. The feature engineering step is performed on the complex networks. The original feature set and/or the features engineered are fed to the proposed stacked ensemble to classify and predict IUI outcome for couples per IUI treatment cycle. Our study is a retrospective study of a 5-year couples' data undergoing IUI. Data is collected from Reproductive Biomedicine Research Center, Royan Institute for 8,360 couples who underwent 11,255 IUI cycles were included. Results: Experimental results show that the proposed method outperforms the compared methods with AUC of 0.84 ± 0.01, sensitivity of 0.79 ± 0.01, specificity of 0.91 ± 0.01, and accuracy of 0.85 ± 0.01 for the prediction of IUI outcome. Conclusions: The most important predictors for predicting IUI outcome are semen parameters (sperm motility and concentration) as well as female BMI.

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