Sociodemographic and clinical characteristics of the study participants.
<p>Sociodemographic and clinical characteristics of the study participants.</p>
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2025
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| _version_ | 1852017208769642496 |
|---|---|
| author | Ju Youn Jung (22139209) |
| author2 | Young Ho Yun (7507208) |
| author2_role | author |
| author_facet | Ju Youn Jung (22139209) Young Ho Yun (7507208) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ju Youn Jung (22139209) Young Ho Yun (7507208) |
| dc.date.none.fl_str_mv | 2025-08-28T17:37:43Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0330570.t001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Sociodemographic_and_clinical_characteristics_of_the_study_participants_/30004618 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biotechnology Cancer Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> despite validate predictive models shapley additive explanation repeated stratified k important features identified final dataset consisted established prediction models creating dependence plots also providing interpretations 42 predictive features specific health outcomes prospective cohort study extreme gradient boost 256 cancer survivors existing prediction model including decision trees overall health status health status separately secondary health statuses xai technique known interpret individual outcomes xgboost predictive model health status health statuses xgboost model survived cancer study represents gradient boosting model comparison appropriate model xgboost ), including physical spiritual well shap ). results using random forest primary objectives management strategies leveraged shap first endeavor critical effects based survey among individuals |
| dc.title.none.fl_str_mv | Sociodemographic and clinical characteristics of the study participants. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>Sociodemographic and clinical characteristics of the study participants.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_2fde11748ea1e8c2eed2d7bcce4272c2 |
| identifier_str_mv | 10.1371/journal.pone.0330570.t001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30004618 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Sociodemographic and clinical characteristics of the study participants.Ju Youn Jung (22139209)Young Ho Yun (7507208)BiotechnologyCancerScience PolicyEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedxlink "> despitevalidate predictive modelsshapley additive explanationrepeated stratified kimportant features identifiedfinal dataset consistedestablished prediction modelscreating dependence plotsalso providing interpretations42 predictive featuresspecific health outcomesprospective cohort studyextreme gradient boost256 cancer survivorsexisting prediction modelincluding decision treesoverall health statushealth status separatelysecondary health statusesxai technique knowninterpret individual outcomesxgboost predictive modelhealth statushealth statusesxgboost modelsurvived cancerstudy representsgradient boostingmodel comparisonappropriate modelxgboost ),including physicalspiritual wellshap ).results usingrandom forestprimary objectivesmanagement strategiesleveraged shapfirst endeavorcritical effectsbased surveyamong individuals<p>Sociodemographic and clinical characteristics of the study participants.</p>2025-08-28T17:37:43ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0330570.t001https://figshare.com/articles/dataset/Sociodemographic_and_clinical_characteristics_of_the_study_participants_/30004618CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/300046182025-08-28T17:37:43Z |
| spellingShingle | Sociodemographic and clinical characteristics of the study participants. Ju Youn Jung (22139209) Biotechnology Cancer Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> despite validate predictive models shapley additive explanation repeated stratified k important features identified final dataset consisted established prediction models creating dependence plots also providing interpretations 42 predictive features specific health outcomes prospective cohort study extreme gradient boost 256 cancer survivors existing prediction model including decision trees overall health status health status separately secondary health statuses xai technique known interpret individual outcomes xgboost predictive model health status health statuses xgboost model survived cancer study represents gradient boosting model comparison appropriate model xgboost ), including physical spiritual well shap ). results using random forest primary objectives management strategies leveraged shap first endeavor critical effects based survey among individuals |
| status_str | publishedVersion |
| title | Sociodemographic and clinical characteristics of the study participants. |
| title_full | Sociodemographic and clinical characteristics of the study participants. |
| title_fullStr | Sociodemographic and clinical characteristics of the study participants. |
| title_full_unstemmed | Sociodemographic and clinical characteristics of the study participants. |
| title_short | Sociodemographic and clinical characteristics of the study participants. |
| title_sort | Sociodemographic and clinical characteristics of the study participants. |
| topic | Biotechnology Cancer Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> despite validate predictive models shapley additive explanation repeated stratified k important features identified final dataset consisted established prediction models creating dependence plots also providing interpretations 42 predictive features specific health outcomes prospective cohort study extreme gradient boost 256 cancer survivors existing prediction model including decision trees overall health status health status separately secondary health statuses xai technique known interpret individual outcomes xgboost predictive model health status health statuses xgboost model survived cancer study represents gradient boosting model comparison appropriate model xgboost ), including physical spiritual well shap ). results using random forest primary objectives management strategies leveraged shap first endeavor critical effects based survey among individuals |