Predicting IVF outcomes using a logistic regression-ABC hybrid model: A proof-of-concept study on supplement associations

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dc.contributor.authorEjder, Ugur
dc.contributor.authorHepsag, Pinar Uskaner
dc.date.accessioned2026-02-27T07:33:28Z
dc.date.available2026-02-27T07:33:28Z
dc.date.issued2025
dc.description.abstractMachine learning models are increasingly applied to assisted reproductive technologies (ART), yet most studies rely on conventional algorithms with limited optimization. This proof-of-concept study investigates whether a hybrid Logistic Regression-Artificial Bee Colony (LR-ABC) framework can enhance predictive performance in in vitro fertilization (IVF) outcomes while producing interpretable, hypothesis-driven associations with nutritional and pharmaceutical supplement use. A retrospective dataset of 162 women undergoing IVF was analyzed. Clinical, demographic, and supplement variables were preprocessed into 21 predictors. Four algorithms (K-Nearest Neighbors, Classification and Regression Tree, Support Vector Machine, and Random Forest) were implemented alongside their LR-ABC hybrid counterparts. Model performance was evaluated using 5-fold cross-validation with Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. Local Interpretable Model-agnostic Explanations (LIME) were applied to improve interpretability. Across all algorithm models, LR-ABC hybrids outperformed their baseline models (e.g., Random Forest: 85.2% -> 91.36% accuracy). LIME explanations identified omega-3, folic acid, and dietician support as influential features in individual predictions. However, given the small sample size, binary representation of supplements, and absence of external validation, the observed improvements and associations should be regarded as exploratory rather than definitive. The LR-ABC hybrid model demonstrates methodological potential for improving prediction and interpretability in IVF research. Findings regarding supplement associations are hypothesis-generating, not clinically directive. Future studies with larger, multi-center datasets including detailed dosage and dietary data are needed to validate and extend this framework.
dc.description.sponsorshipTubitak
dc.description.sponsorshipThis study was supported by the Scientific and Technological Research Council of Turkey (TUB & Idot;TAK) through a publication incentive program. TUB & Idot;TAK had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
dc.identifier.doi10.1371/journal.pone.0336846
dc.identifier.issn1932-6203
dc.identifier.issue11
dc.identifier.pmid41289279
dc.identifier.urihttp://dx.doi.org/10.1371/journal.pone.0336846
dc.identifier.urihttps://hdl.handle.net/20.500.14669/4596
dc.identifier.volume20
dc.identifier.wosWOS:001625259900033
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherPublic Library Science
dc.relation.ispartofPlos One
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20260302
dc.titlePredicting IVF outcomes using a logistic regression-ABC hybrid model: A proof-of-concept study on supplement associations
dc.typeArticle

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