Developing machine learning-based models to predict intrauterine insemination (IUI) success by address modeling challenges in imbalanced data and providing modification solutions for them.
Background: This study sought to provide machine learning-based classification models to predict the success of intrauterine insemination (IUI) therapy. Additionally, we sought to illustrate the effect of models fitting with balanced data vs original data with imbalanced data labels using two differ...
| Published in: | BMC Medical Informatics & Decision Making Vol. 22; no. 1; pp. 1 - 14 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Published: |
BioMed Central
9/1/2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=158853583&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158853583 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 9/1/2022 vid: 22 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 158853583 158853583 NLM36050710 10.1186/s12911-022-01974-8 NLM36050710 158853583 ppf: 1 ppct: 13 formats: tig: atl: Developing machine learning-based models to predict intrauterine insemination (IUI) success by address modeling challenges in imbalanced data and providing modification solutions for them. aug: au: Khodabandelu, Sajad Basirat, Zahra Khaleghi, Sara Khafri, Soraya Montazery Kordy, Hussain Golsorkhtabaramiri, Masoumeh affil: Student Research Committee, Babol University of Medical Sciences, Babol, Iran sug: subj: Sperm Motility Semen Female Cross Sectional Studies Young Adult Insemination, Artificial Reproduction Male Adult Pregnancy Adult: 19-44 years Female Male ab: Background: This study sought to provide machine learning-based classification models to predict the success of intrauterine insemination (IUI) therapy. Additionally, we sought to illustrate the effect of models fitting with balanced data vs original data with imbalanced data labels using two different types of resampling methods. Finally, we fit models with all features against optimized feature sets using various feature selection techniques.Methods: The data for the cross-sectional study were collected from 546 infertile couples with IUI at the Fatemehzahra Infertility Research Center, Babol, North of Iran. Logistic regression (LR), support vector classification, random forest, Extreme Gradient Boosting (XGBoost) and, Stacking generalization (Stack) as the machine learning classifiers were used to predict IUI success by Python v3.7. We employed the Smote-Tomek (Stomek) and Smote-ENN (SENN) resampling methods to address the imbalance problem in the original dataset. Furthermore, to increase the performance of the models, mutual information classification (MIC-FS), genetic algorithm (GA-FS), and random forest (RF-FS) were used to select the ideal feature sets for model development.Results: In this study, 28% of patients undergoing IUI treatment obtained a successful pregnancy. Also, the average age of women and men was 24.98 and 29.85 years, respectively. The calibration plot in this study for IUI success prediction by machine learning models showed that between feature selection methods, the RF-FS, and among the datasets used to fit the models, the balanced dataset with the Stomek method had well-calibrating predictions than other methods. Finally, the brier scores for the LR, SVC, RF, XGBoost, and Stack models that were fitted utilizing the Stomek dataset and the chosen feature set using the Random Forest technique obtained equal to 0.202, 0.183, 0.158, 0.129, and 0.134, respectively. It showed duration of infertility, male and female age, sperm concentration, and sperm motility grading score as the most predictable factors in IUI success.Conclusion: The results of this study with the XGBoost prediction model can be used to foretell the individual success of IUI for each couple before initiating therapy. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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