Raw performance metrics for the Random Forest (RF) model.
<p>The table presents the raw output from the bootstrap analysis, showing the mean, lower, and upper confidence interval bounds for the AUC of local and federated models, and the performance gain (<i>Δ</i>AUC). Similar to the preceding tables, it presents the mean AUC and 95% CIs f...
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| _version_ | 1849927641227853824 |
|---|---|
| author | Roberta Moreira Wichmann (14259316) |
| author2 | Murilo Afonso Robiati Bigoto (22676715) Alexandre Dias Porto Chiavegatto Filho (14259328) |
| author2_role | author author |
| author_facet | Roberta Moreira Wichmann (14259316) Murilo Afonso Robiati Bigoto (22676715) Alexandre Dias Porto Chiavegatto Filho (14259328) |
| author_role | author |
| dc.creator.none.fl_str_mv | Roberta Moreira Wichmann (14259316) Murilo Afonso Robiati Bigoto (22676715) Alexandre Dias Porto Chiavegatto Filho (14259328) |
| dc.date.none.fl_str_mv | 2025-11-24T18:35:37Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pcbi.1013695.s004 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Raw_performance_metrics_for_the_Random_Forest_RF_model_/30697797 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Cell Biology Ecology Cancer Science Policy Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified via parameter aggregation using ensemble aggregation strong inverse correlation maximum performance degradation benefits requires caution beneficial collaborative effect >&# 916 ;</ 19 mortality using 19 mortality prediction local patient volume statistically certain benefits statistical significance showed 0273 ]) due auc crossed zero study validates fl fl models demonstrated performance gain (< evaluated federated learning div >< p collaborative model ’ auc minus auc local data scarcity ci [– 0 gain ’ federated learning local validation universally certain statistical advantage 6307 ]) auc across – 0 volatile algorithm smallest hospital particularly evident network (< multicentric sample multicenter sample limited institutions layer perceptron iid ). high sensitivity findings underscore every site enabling mechanism despite achieving confidence intervals 500 ). 022 patients |
| dc.title.none.fl_str_mv | Raw performance metrics for the Random Forest (RF) model. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>The table presents the raw output from the bootstrap analysis, showing the mean, lower, and upper confidence interval bounds for the AUC of local and federated models, and the performance gain (<i>Δ</i>AUC). Similar to the preceding tables, it presents the mean AUC and 95% CIs for the local and federated models at each hospital. The performance gain, measured by the <i>Δ</i>AUC and its 95% CI, is also shown, offering a detailed comparison for this ensemble-based learning method.</p> <p>(XLSX)</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_4607e4d9ab8696498c4d000d2173e239 |
| identifier_str_mv | 10.1371/journal.pcbi.1013695.s004 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30697797 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Raw performance metrics for the Random Forest (RF) model.Roberta Moreira Wichmann (14259316)Murilo Afonso Robiati Bigoto (22676715)Alexandre Dias Porto Chiavegatto Filho (14259328)Cell BiologyEcologyCancerScience PolicyBiological Sciences not elsewhere classifiedChemical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedvia parameter aggregationusing ensemble aggregationstrong inverse correlationmaximum performance degradationbenefits requires cautionbeneficial collaborative effect>&# 916 ;</19 mortality using19 mortality predictionlocal patient volumestatistically certain benefitsstatistical significance showed0273 ]) dueauc crossed zerostudy validates flfl models demonstratedperformance gain (<evaluated federated learningdiv >< pcollaborative model ’auc minus auclocal data scarcityci [– 0gain ’federated learninglocal validationuniversally certainstatistical advantage6307 ])auc across– 0volatile algorithmsmallest hospitalparticularly evidentnetwork (<multicentric samplemulticenter samplelimited institutionslayer perceptroniid ).high sensitivityfindings underscoreevery siteenabling mechanismdespite achievingconfidence intervals500 ).022 patients<p>The table presents the raw output from the bootstrap analysis, showing the mean, lower, and upper confidence interval bounds for the AUC of local and federated models, and the performance gain (<i>Δ</i>AUC). Similar to the preceding tables, it presents the mean AUC and 95% CIs for the local and federated models at each hospital. The performance gain, measured by the <i>Δ</i>AUC and its 95% CI, is also shown, offering a detailed comparison for this ensemble-based learning method.</p> <p>(XLSX)</p>2025-11-24T18:35:37ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pcbi.1013695.s004https://figshare.com/articles/dataset/Raw_performance_metrics_for_the_Random_Forest_RF_model_/30697797CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/306977972025-11-24T18:35:37Z |
| spellingShingle | Raw performance metrics for the Random Forest (RF) model. Roberta Moreira Wichmann (14259316) Cell Biology Ecology Cancer Science Policy Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified via parameter aggregation using ensemble aggregation strong inverse correlation maximum performance degradation benefits requires caution beneficial collaborative effect >&# 916 ;</ 19 mortality using 19 mortality prediction local patient volume statistically certain benefits statistical significance showed 0273 ]) due auc crossed zero study validates fl fl models demonstrated performance gain (< evaluated federated learning div >< p collaborative model ’ auc minus auc local data scarcity ci [– 0 gain ’ federated learning local validation universally certain statistical advantage 6307 ]) auc across – 0 volatile algorithm smallest hospital particularly evident network (< multicentric sample multicenter sample limited institutions layer perceptron iid ). high sensitivity findings underscore every site enabling mechanism despite achieving confidence intervals 500 ). 022 patients |
| status_str | publishedVersion |
| title | Raw performance metrics for the Random Forest (RF) model. |
| title_full | Raw performance metrics for the Random Forest (RF) model. |
| title_fullStr | Raw performance metrics for the Random Forest (RF) model. |
| title_full_unstemmed | Raw performance metrics for the Random Forest (RF) model. |
| title_short | Raw performance metrics for the Random Forest (RF) model. |
| title_sort | Raw performance metrics for the Random Forest (RF) model. |
| topic | Cell Biology Ecology Cancer Science Policy Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified via parameter aggregation using ensemble aggregation strong inverse correlation maximum performance degradation benefits requires caution beneficial collaborative effect >&# 916 ;</ 19 mortality using 19 mortality prediction local patient volume statistically certain benefits statistical significance showed 0273 ]) due auc crossed zero study validates fl fl models demonstrated performance gain (< evaluated federated learning div >< p collaborative model ’ auc minus auc local data scarcity ci [– 0 gain ’ federated learning local validation universally certain statistical advantage 6307 ]) auc across – 0 volatile algorithm smallest hospital particularly evident network (< multicentric sample multicenter sample limited institutions layer perceptron iid ). high sensitivity findings underscore every site enabling mechanism despite achieving confidence intervals 500 ). 022 patients |