Flow diagram of the proposed model.
<div><p>Machine 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) f...
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| _version_ | 1849927629993410560 |
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| author | Uğur Ejder (22683228) |
| author2 | Pınar Uskaner Hepsağ (22683231) |
| author2_role | author |
| author_facet | Uğur Ejder (22683228) Pınar Uskaner Hepsağ (22683231) |
| author_role | author |
| dc.creator.none.fl_str_mv | Uğur Ejder (22683228) Pınar Uskaner Hepsağ (22683231) |
| dc.date.none.fl_str_mv | 2025-11-25T18:23:56Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0336846.g002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Flow_diagram_of_the_proposed_model_/30713256 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biotechnology Ecology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified small sample size lr &# 8211 evaluated using 5 assisted reproductive technologies artificial bee colony art ), yet address class imbalance support vector machine pharmaceutical supplement use enhance predictive performance abc hybrids outperformed abc hybrid counterparts local interpretable model abc hybrid model model performance supplement variables producing interpretable dietician support vitro fertilization synthetic minority studies rely sampling technique retrospective dataset regression tree random forest observed improvements nearest neighbors limited optimization influential features individual predictions improving prediction implemented alongside future studies four algorithms folic acid fold cross exploratory rather dietary data conventional algorithms concept study clinically directive binary representation baseline models algorithm models agnostic explanations accuracy ). 21 predictors |
| dc.title.none.fl_str_mv | Flow diagram of the proposed model. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>Machine 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.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_747b63e615fffd2a134224cdce96cb6e |
| identifier_str_mv | 10.1371/journal.pone.0336846.g002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30713256 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Flow diagram of the proposed model.Uğur Ejder (22683228)Pınar Uskaner Hepsağ (22683231)BiotechnologyEcologyCancerBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedsmall sample sizelr &# 8211evaluated using 5assisted reproductive technologiesartificial bee colonyart ), yetaddress class imbalancesupport vector machinepharmaceutical supplement useenhance predictive performanceabc hybrids outperformedabc hybrid counterpartslocal interpretable modelabc hybrid modelmodel performancesupplement variablesproducing interpretabledietician supportvitro fertilizationsynthetic minoritystudies relysampling techniqueretrospective datasetregression treerandom forestobserved improvementsnearest neighborslimited optimizationinfluential featuresindividual predictionsimproving predictionimplemented alongsidefuture studiesfour algorithmsfolic acidfold crossexploratory ratherdietary dataconventional algorithmsconcept studyclinically directivebinary representationbaseline modelsalgorithm modelsagnostic explanationsaccuracy ).21 predictors<div><p>Machine 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.</p></div>2025-11-25T18:23:56ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0336846.g002https://figshare.com/articles/figure/Flow_diagram_of_the_proposed_model_/30713256CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/307132562025-11-25T18:23:56Z |
| spellingShingle | Flow diagram of the proposed model. Uğur Ejder (22683228) Biotechnology Ecology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified small sample size lr &# 8211 evaluated using 5 assisted reproductive technologies artificial bee colony art ), yet address class imbalance support vector machine pharmaceutical supplement use enhance predictive performance abc hybrids outperformed abc hybrid counterparts local interpretable model abc hybrid model model performance supplement variables producing interpretable dietician support vitro fertilization synthetic minority studies rely sampling technique retrospective dataset regression tree random forest observed improvements nearest neighbors limited optimization influential features individual predictions improving prediction implemented alongside future studies four algorithms folic acid fold cross exploratory rather dietary data conventional algorithms concept study clinically directive binary representation baseline models algorithm models agnostic explanations accuracy ). 21 predictors |
| status_str | publishedVersion |
| title | Flow diagram of the proposed model. |
| title_full | Flow diagram of the proposed model. |
| title_fullStr | Flow diagram of the proposed model. |
| title_full_unstemmed | Flow diagram of the proposed model. |
| title_short | Flow diagram of the proposed model. |
| title_sort | Flow diagram of the proposed model. |
| topic | Biotechnology Ecology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified small sample size lr &# 8211 evaluated using 5 assisted reproductive technologies artificial bee colony art ), yet address class imbalance support vector machine pharmaceutical supplement use enhance predictive performance abc hybrids outperformed abc hybrid counterparts local interpretable model abc hybrid model model performance supplement variables producing interpretable dietician support vitro fertilization synthetic minority studies rely sampling technique retrospective dataset regression tree random forest observed improvements nearest neighbors limited optimization influential features individual predictions improving prediction implemented alongside future studies four algorithms folic acid fold cross exploratory rather dietary data conventional algorithms concept study clinically directive binary representation baseline models algorithm models agnostic explanations accuracy ). 21 predictors |