Comparison of binary classifier performance among four machine learning models for early-loss, intermediate-loss, and late/no-loss groups.
<p>Comparison of binary classifier performance among four machine learning models for early-loss, intermediate-loss, and late/no-loss groups.</p>
Shranjeno v:
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| Drugi avtorji: | , , , , , , , |
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2025
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| _version_ | 1849927642259652608 |
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| author | Raiki Yoshimura (22676573) |
| author2 | Naotoshi Nakamura (12083048) Takeru Matsuura (22676576) Takeo Toshima (13988776) Takasuke Fukuhara (673475) Kazuyuki Aihara (44090) Katsuhito Fujiu (559488) Shingo Iwami (266092) Tomoharu Yoshizumi (2621434) |
| author2_role | author author author author author author author author |
| author_facet | Raiki Yoshimura (22676573) Naotoshi Nakamura (12083048) Takeru Matsuura (22676576) Takeo Toshima (13988776) Takasuke Fukuhara (673475) Kazuyuki Aihara (44090) Katsuhito Fujiu (559488) Shingo Iwami (266092) Tomoharu Yoshizumi (2621434) |
| author_role | author |
| dc.creator.none.fl_str_mv | Raiki Yoshimura (22676573) Naotoshi Nakamura (12083048) Takeru Matsuura (22676576) Takeo Toshima (13988776) Takasuke Fukuhara (673475) Kazuyuki Aihara (44090) Katsuhito Fujiu (559488) Shingo Iwami (266092) Tomoharu Yoshizumi (2621434) |
| dc.date.none.fl_str_mv | 2025-11-24T18:30:49Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pcbi.1013734.t004 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Comparison_of_binary_classifier_performance_among_four_machine_learning_models_for_early-loss_intermediate-loss_and_late_no-loss_groups_/30697316 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Medicine urgent need exists stage liver disease shorter waiting times recent years thanks populations using data model enabled us highly heterogeneous sample appropriate patient care 30 days postoperatively distinct population similar different survival times hierarchical prediction method better graft quality survival time loss population graft loss better performance unexpected infections transplanted organ three groups several models next categorized mediated rejection loss groups loss group living donors gained importance five groups conventional models |
| dc.title.none.fl_str_mv | Comparison of binary classifier performance among four machine learning models for early-loss, intermediate-loss, and late/no-loss groups. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>Comparison of binary classifier performance among four machine learning models for early-loss, intermediate-loss, and late/no-loss groups.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_5901a72104ffc502137fd52232de9e72 |
| identifier_str_mv | 10.1371/journal.pcbi.1013734.t004 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30697316 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Comparison of binary classifier performance among four machine learning models for early-loss, intermediate-loss, and late/no-loss groups.Raiki Yoshimura (22676573)Naotoshi Nakamura (12083048)Takeru Matsuura (22676576)Takeo Toshima (13988776)Takasuke Fukuhara (673475)Kazuyuki Aihara (44090)Katsuhito Fujiu (559488)Shingo Iwami (266092)Tomoharu Yoshizumi (2621434)Medicineurgent need existsstage liver diseaseshorter waiting timesrecent years thankspopulations using datamodel enabled ushighly heterogeneous sampleappropriate patient care30 days postoperativelydistinct population similardifferent survival timeshierarchical prediction methodbetter graft qualitysurvival timeloss populationgraft lossbetter performanceunexpected infectionstransplanted organthree groupsseveral modelsnext categorizedmediated rejectionloss groupsloss groupliving donorsgained importancefive groupsconventional models<p>Comparison of binary classifier performance among four machine learning models for early-loss, intermediate-loss, and late/no-loss groups.</p>2025-11-24T18:30:49ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pcbi.1013734.t004https://figshare.com/articles/dataset/Comparison_of_binary_classifier_performance_among_four_machine_learning_models_for_early-loss_intermediate-loss_and_late_no-loss_groups_/30697316CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/306973162025-11-24T18:30:49Z |
| spellingShingle | Comparison of binary classifier performance among four machine learning models for early-loss, intermediate-loss, and late/no-loss groups. Raiki Yoshimura (22676573) Medicine urgent need exists stage liver disease shorter waiting times recent years thanks populations using data model enabled us highly heterogeneous sample appropriate patient care 30 days postoperatively distinct population similar different survival times hierarchical prediction method better graft quality survival time loss population graft loss better performance unexpected infections transplanted organ three groups several models next categorized mediated rejection loss groups loss group living donors gained importance five groups conventional models |
| status_str | publishedVersion |
| title | Comparison of binary classifier performance among four machine learning models for early-loss, intermediate-loss, and late/no-loss groups. |
| title_full | Comparison of binary classifier performance among four machine learning models for early-loss, intermediate-loss, and late/no-loss groups. |
| title_fullStr | Comparison of binary classifier performance among four machine learning models for early-loss, intermediate-loss, and late/no-loss groups. |
| title_full_unstemmed | Comparison of binary classifier performance among four machine learning models for early-loss, intermediate-loss, and late/no-loss groups. |
| title_short | Comparison of binary classifier performance among four machine learning models for early-loss, intermediate-loss, and late/no-loss groups. |
| title_sort | Comparison of binary classifier performance among four machine learning models for early-loss, intermediate-loss, and late/no-loss groups. |
| topic | Medicine urgent need exists stage liver disease shorter waiting times recent years thanks populations using data model enabled us highly heterogeneous sample appropriate patient care 30 days postoperatively distinct population similar different survival times hierarchical prediction method better graft quality survival time loss population graft loss better performance unexpected infections transplanted organ three groups several models next categorized mediated rejection loss groups loss group living donors gained importance five groups conventional models |