The statistical description of the original data set of the patients (n = 162).

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Autore principale:	Uğur Ejder (22683228) (author)
Altri autori:	Pınar Uskaner Hepsağ (22683231) (author)
Pubblicazione:	2025
Soggetti:	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
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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	2025-11-25T18:24:05Z
dc.identifier.none.fl_str_mv	10.1371/journal.pone.0336846.t001
dc.relation.none.fl_str_mv	https://figshare.com/articles/dataset/The_statistical_description_of_the_original_data_set_of_the_patients_i_n_i_162_/30713280
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	The statistical description of the original data set of the patients (<i>n</i> = 162).
dc.type.none.fl_str_mv	Dataset info:eu-repo/semantics/publishedVersion dataset
description	<p>The statistical description of the original data set of the patients (<i>n</i> = 162).</p>
eu_rights_str_mv	openAccess
id	Manara_86a45ee958802f8c4ef21fcb167ad963
identifier_str_mv	10.1371/journal.pone.0336846.t001
network_acronym_str	Manara
network_name_str	ManaraRepo
oai_identifier_str	oai:figshare.com:article/30713280
publishDate	2025
repository.mail.fl_str_mv
repository.name.fl_str_mv
repository_id_str
rights_invalid_str_mv	CC BY 4.0
spelling	The statistical description of the original data set of the patients (<i>n</i> = 162).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<p>The statistical description of the original data set of the patients (<i>n</i> = 162).</p>2025-11-25T18:24:05ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0336846.t001https://figshare.com/articles/dataset/The_statistical_description_of_the_original_data_set_of_the_patients_i_n_i_162_/30713280CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/307132802025-11-25T18:24:05Z
spellingShingle	The statistical description of the original data set of the patients (<i>n</i> = 162). 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	The statistical description of the original data set of the patients (<i>n</i> = 162).
title_full	The statistical description of the original data set of the patients (<i>n</i> = 162).
title_fullStr	The statistical description of the original data set of the patients (<i>n</i> = 162).
title_full_unstemmed	The statistical description of the original data set of the patients (<i>n</i> = 162).
title_short	The statistical description of the original data set of the patients (<i>n</i> = 162).
title_sort	The statistical description of the original data set of the patients (<i>n</i> = 162).
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

The statistical description of the original data set of the patients (<i>n</i> = 162).

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