Comparison of Machine Learning Methods in the Diabetes-Prediction-Analysis dataset. We mark the top three results in bold.
<p>Comparison of Machine Learning Methods in the Diabetes-Prediction-Analysis dataset. We mark the top three results in bold.</p>
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2025
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| _version_ | 1852018657364803584 |
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| author | Wangyouchen Zhang (21681025) |
| author2 | Zhenhua Xia (582939) Guoqing Cai (740684) Junhao Wang (751227) Xutao Dong (21681028) |
| author2_role | author author author author |
| author_facet | Wangyouchen Zhang (21681025) Zhenhua Xia (582939) Guoqing Cai (740684) Junhao Wang (751227) Xutao Dong (21681028) |
| author_role | author |
| dc.creator.none.fl_str_mv | Wangyouchen Zhang (21681025) Zhenhua Xia (582939) Guoqing Cai (740684) Junhao Wang (751227) Xutao Dong (21681028) |
| dc.date.none.fl_str_mv | 2025-07-08T17:32:39Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0327120.t003 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Comparison_of_Machine_Learning_Methods_in_the_Diabetes-Prediction-Analysis_dataset_We_mark_the_top_three_results_in_bold_/29507290 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biochemistry Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified typically achieve 95 smaller labeled dataset shapley additive explanations reducing labeling costs quantify feature importance overcome common challenges outperforming traditional models model &# 8217 machine learning models focal active learning fewer labeled samples experimental results demonstrated enhancing model interpretability efficient data utilization called foci ), better prediction outcomes applies attention mechanisms predicting diabetes risk diabetes risk prediction minority class instances method integrates shap imbalanced medical datasets approach significantly improved novel method based diabetes datasets based method minority classes method aims imbalanced classification based sampling xlink "> using similarity study proposes limited generalization iteratively constructed generalization ability evaluation metrics diabetic cases clinical settings |
| dc.title.none.fl_str_mv | Comparison of Machine Learning Methods in the Diabetes-Prediction-Analysis dataset. We mark the top three results in bold. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>Comparison of Machine Learning Methods in the Diabetes-Prediction-Analysis dataset. We mark the top three results in bold.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_3ccd0d1f19226b2ead4e041d549dabea |
| identifier_str_mv | 10.1371/journal.pone.0327120.t003 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29507290 |
| 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 Machine Learning Methods in the Diabetes-Prediction-Analysis dataset. We mark the top three results in bold.Wangyouchen Zhang (21681025)Zhenhua Xia (582939)Guoqing Cai (740684)Junhao Wang (751227)Xutao Dong (21681028)BiochemistryBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedtypically achieve 95smaller labeled datasetshapley additive explanationsreducing labeling costsquantify feature importanceovercome common challengesoutperforming traditional modelsmodel &# 8217machine learning modelsfocal active learningfewer labeled samplesexperimental results demonstratedenhancing model interpretabilityefficient data utilizationcalled foci ),better prediction outcomesapplies attention mechanismspredicting diabetes riskdiabetes risk predictionminority class instancesmethod integrates shapimbalanced medical datasetsapproach significantly improvednovel method baseddiabetes datasetsbased methodminority classesmethod aimsimbalanced classificationbased samplingxlink ">using similaritystudy proposeslimited generalizationiteratively constructedgeneralization abilityevaluation metricsdiabetic casesclinical settings<p>Comparison of Machine Learning Methods in the Diabetes-Prediction-Analysis dataset. We mark the top three results in bold.</p>2025-07-08T17:32:39ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0327120.t003https://figshare.com/articles/dataset/Comparison_of_Machine_Learning_Methods_in_the_Diabetes-Prediction-Analysis_dataset_We_mark_the_top_three_results_in_bold_/29507290CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/295072902025-07-08T17:32:39Z |
| spellingShingle | Comparison of Machine Learning Methods in the Diabetes-Prediction-Analysis dataset. We mark the top three results in bold. Wangyouchen Zhang (21681025) Biochemistry Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified typically achieve 95 smaller labeled dataset shapley additive explanations reducing labeling costs quantify feature importance overcome common challenges outperforming traditional models model &# 8217 machine learning models focal active learning fewer labeled samples experimental results demonstrated enhancing model interpretability efficient data utilization called foci ), better prediction outcomes applies attention mechanisms predicting diabetes risk diabetes risk prediction minority class instances method integrates shap imbalanced medical datasets approach significantly improved novel method based diabetes datasets based method minority classes method aims imbalanced classification based sampling xlink "> using similarity study proposes limited generalization iteratively constructed generalization ability evaluation metrics diabetic cases clinical settings |
| status_str | publishedVersion |
| title | Comparison of Machine Learning Methods in the Diabetes-Prediction-Analysis dataset. We mark the top three results in bold. |
| title_full | Comparison of Machine Learning Methods in the Diabetes-Prediction-Analysis dataset. We mark the top three results in bold. |
| title_fullStr | Comparison of Machine Learning Methods in the Diabetes-Prediction-Analysis dataset. We mark the top three results in bold. |
| title_full_unstemmed | Comparison of Machine Learning Methods in the Diabetes-Prediction-Analysis dataset. We mark the top three results in bold. |
| title_short | Comparison of Machine Learning Methods in the Diabetes-Prediction-Analysis dataset. We mark the top three results in bold. |
| title_sort | Comparison of Machine Learning Methods in the Diabetes-Prediction-Analysis dataset. We mark the top three results in bold. |
| topic | Biochemistry Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified typically achieve 95 smaller labeled dataset shapley additive explanations reducing labeling costs quantify feature importance overcome common challenges outperforming traditional models model &# 8217 machine learning models focal active learning fewer labeled samples experimental results demonstrated enhancing model interpretability efficient data utilization called foci ), better prediction outcomes applies attention mechanisms predicting diabetes risk diabetes risk prediction minority class instances method integrates shap imbalanced medical datasets approach significantly improved novel method based diabetes datasets based method minority classes method aims imbalanced classification based sampling xlink "> using similarity study proposes limited generalization iteratively constructed generalization ability evaluation metrics diabetic cases clinical settings |