Hypertension distribution in the original dataset, and balanced class label distribution after applying the Synthetic Minority Oversampling Technique (SMOTE) for training and test data.
<p>Hypertension distribution in the original dataset, and balanced class label distribution after applying the Synthetic Minority Oversampling Technique (SMOTE) for training and test data.</p>
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2025
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| _version_ | 1852018779071971328 |
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| author | Probir Kumar Ghosh (6947282) |
| author2 | Md. Aminul Islam (6750650) Md. Ahshanul Haque (10525794) Md. Tariqujjaman (7038959) Novel Chandra Das (19742953) Mohammad Ali (73095) Md. Rasel Uddin (21648057) Md. Golam Dostogir Harun (7208396) |
| author2_role | author author author author author author author |
| author_facet | Probir Kumar Ghosh (6947282) Md. Aminul Islam (6750650) Md. Ahshanul Haque (10525794) Md. Tariqujjaman (7038959) Novel Chandra Das (19742953) Mohammad Ali (73095) Md. Rasel Uddin (21648057) Md. Golam Dostogir Harun (7208396) |
| author_role | author |
| dc.creator.none.fl_str_mv | Probir Kumar Ghosh (6947282) Md. Aminul Islam (6750650) Md. Ahshanul Haque (10525794) Md. Tariqujjaman (7038959) Novel Chandra Das (19742953) Mohammad Ali (73095) Md. Rasel Uddin (21648057) Md. Golam Dostogir Harun (7208396) |
| dc.date.none.fl_str_mv | 2025-07-02T18:00:59Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pcbi.1013211.g002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Hypertension_distribution_in_the_original_dataset_and_balanced_class_label_distribution_after_applying_the_Synthetic_Minority_Oversampling_Technique_SMOTE_for_training_and_test_data_/29464125 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Medicine Biotechnology Computational Biology Mathematical Sciences not elsewhere classified systolic blood pressure shapley additive explanations refined risk estimate random forest model individual conditional expectation estimating causal relationships double machine learning diastolic blood pressure assessing causal effect 90 &# 8201 79 &# 8211 140 &# 8201 07 &# 8211 body mass index used logistic regression nonlinear observed confounders dataset included 11 biased methods provided 17 &# 8211 excessive body weight shap plots reveal identified older age health surveys data demonstrating complex interactions ses ), self body weight used 11 ses ), 17 ), nonlinear relation health population biased method dml ), hypertension data xlink "> targeted interventions study aims socioeconomic status significantly contributed shown effectiveness mm hg limited settings key predictors income countries including age identifying predictors identify predictors female gender evaluation f1 average age |
| dc.title.none.fl_str_mv | Hypertension distribution in the original dataset, and balanced class label distribution after applying the Synthetic Minority Oversampling Technique (SMOTE) for training and test data. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>Hypertension distribution in the original dataset, and balanced class label distribution after applying the Synthetic Minority Oversampling Technique (SMOTE) for training and test data.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_4dfc183261b8a6ceebabac3bab40fd39 |
| identifier_str_mv | 10.1371/journal.pcbi.1013211.g002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29464125 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Hypertension distribution in the original dataset, and balanced class label distribution after applying the Synthetic Minority Oversampling Technique (SMOTE) for training and test data.Probir Kumar Ghosh (6947282)Md. Aminul Islam (6750650)Md. Ahshanul Haque (10525794)Md. Tariqujjaman (7038959)Novel Chandra Das (19742953)Mohammad Ali (73095)Md. Rasel Uddin (21648057)Md. Golam Dostogir Harun (7208396)MedicineBiotechnologyComputational BiologyMathematical Sciences not elsewhere classifiedsystolic blood pressureshapley additive explanationsrefined risk estimaterandom forest modelindividual conditional expectationestimating causal relationshipsdouble machine learningdiastolic blood pressureassessing causal effect90 &# 820179 &# 8211140 &# 820107 &# 8211body mass indexused logistic regressionnonlinear observed confoundersdataset included 11biased methods provided17 &# 8211excessive body weightshap plots revealidentified older agehealth surveys datademonstrating complex interactionsses ), selfbody weightused 11ses ),17 ),nonlinear relationhealth populationbiased methoddml ),hypertension dataxlink ">targeted interventionsstudy aimssocioeconomic statussignificantly contributedshown effectivenessmm hglimited settingskey predictorsincome countriesincluding ageidentifying predictorsidentify predictorsfemale genderevaluation f1average age<p>Hypertension distribution in the original dataset, and balanced class label distribution after applying the Synthetic Minority Oversampling Technique (SMOTE) for training and test data.</p>2025-07-02T18:00:59ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pcbi.1013211.g002https://figshare.com/articles/figure/Hypertension_distribution_in_the_original_dataset_and_balanced_class_label_distribution_after_applying_the_Synthetic_Minority_Oversampling_Technique_SMOTE_for_training_and_test_data_/29464125CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/294641252025-07-02T18:00:59Z |
| spellingShingle | Hypertension distribution in the original dataset, and balanced class label distribution after applying the Synthetic Minority Oversampling Technique (SMOTE) for training and test data. Probir Kumar Ghosh (6947282) Medicine Biotechnology Computational Biology Mathematical Sciences not elsewhere classified systolic blood pressure shapley additive explanations refined risk estimate random forest model individual conditional expectation estimating causal relationships double machine learning diastolic blood pressure assessing causal effect 90 &# 8201 79 &# 8211 140 &# 8201 07 &# 8211 body mass index used logistic regression nonlinear observed confounders dataset included 11 biased methods provided 17 &# 8211 excessive body weight shap plots reveal identified older age health surveys data demonstrating complex interactions ses ), self body weight used 11 ses ), 17 ), nonlinear relation health population biased method dml ), hypertension data xlink "> targeted interventions study aims socioeconomic status significantly contributed shown effectiveness mm hg limited settings key predictors income countries including age identifying predictors identify predictors female gender evaluation f1 average age |
| status_str | publishedVersion |
| title | Hypertension distribution in the original dataset, and balanced class label distribution after applying the Synthetic Minority Oversampling Technique (SMOTE) for training and test data. |
| title_full | Hypertension distribution in the original dataset, and balanced class label distribution after applying the Synthetic Minority Oversampling Technique (SMOTE) for training and test data. |
| title_fullStr | Hypertension distribution in the original dataset, and balanced class label distribution after applying the Synthetic Minority Oversampling Technique (SMOTE) for training and test data. |
| title_full_unstemmed | Hypertension distribution in the original dataset, and balanced class label distribution after applying the Synthetic Minority Oversampling Technique (SMOTE) for training and test data. |
| title_short | Hypertension distribution in the original dataset, and balanced class label distribution after applying the Synthetic Minority Oversampling Technique (SMOTE) for training and test data. |
| title_sort | Hypertension distribution in the original dataset, and balanced class label distribution after applying the Synthetic Minority Oversampling Technique (SMOTE) for training and test data. |
| topic | Medicine Biotechnology Computational Biology Mathematical Sciences not elsewhere classified systolic blood pressure shapley additive explanations refined risk estimate random forest model individual conditional expectation estimating causal relationships double machine learning diastolic blood pressure assessing causal effect 90 &# 8201 79 &# 8211 140 &# 8201 07 &# 8211 body mass index used logistic regression nonlinear observed confounders dataset included 11 biased methods provided 17 &# 8211 excessive body weight shap plots reveal identified older age health surveys data demonstrating complex interactions ses ), self body weight used 11 ses ), 17 ), nonlinear relation health population biased method dml ), hypertension data xlink "> targeted interventions study aims socioeconomic status significantly contributed shown effectiveness mm hg limited settings key predictors income countries including age identifying predictors identify predictors female gender evaluation f1 average age |