Ranking of features for each algorithm.
<div><p>Stroke analysis using game theory and machine learning techniques. The study investigates the use of the Shapley value in predictive ischemic brain stroke analysis. Initially, preference algorithms identify the most important features in various machine learning models, including...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , , , |
| منشور في: |
2025
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1852017639290830848 |
|---|---|
| author | Pritam Chakraborty (9261302) |
| author2 | Anjan Bandyopadhyay (19541099) Sricheta Parui (22049880) Sujata Swain (22049883) Partha Sarathy Banerjee (22049886) Tapas Si (12308990) Hong Qin (279614) Saurav Mallik (441729) |
| author2_role | author author author author author author author |
| author_facet | Pritam Chakraborty (9261302) Anjan Bandyopadhyay (19541099) Sricheta Parui (22049880) Sujata Swain (22049883) Partha Sarathy Banerjee (22049886) Tapas Si (12308990) Hong Qin (279614) Saurav Mallik (441729) |
| author_role | author |
| dc.creator.none.fl_str_mv | Pritam Chakraborty (9261302) Anjan Bandyopadhyay (19541099) Sricheta Parui (22049880) Sujata Swain (22049883) Partha Sarathy Banerjee (22049886) Tapas Si (12308990) Hong Qin (279614) Saurav Mallik (441729) |
| dc.date.none.fl_str_mv | 2025-08-13T19:10:38Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0328967.t003 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Ranking_of_features_for_each_algorithm_/29903532 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified preference algorithms identify ischemic stroke prediction including logistic regression 39 %, surpassing support vector machine ensemble learning methods machine learning techniques integrating machine learning accurate predictions using increase predictive accuracy combining game theory shapley value method machine learning game theory shapley value predictive capabilities models using impressive accuracy shapely value top 3 study investigates study highlights research demonstrates performing models nearest neighbor important features decision tree 5 features |
| dc.title.none.fl_str_mv | Ranking of features for each algorithm. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>Stroke analysis using game theory and machine learning techniques. The study investigates the use of the Shapley value in predictive ischemic brain stroke analysis. Initially, preference algorithms identify the most important features in various machine learning models, including logistic regression, K-nearest neighbor, decision tree, support vector machine (linear kernel), support vector machine ( RBF kernel), neural networks, etc. For each sample, the top 3, 4, and 5 features are evaluated and selected to evaluate their performance. The Shapley value method was used to rank the models using their best four features based on their predictive capabilities. As a result, better-performing models were found. Afterward, ensemble machine learning methods were used to find the most accurate predictions using the top 5 models ranked by shapely value. The research demonstrates an impressive accuracy of 92.39%, surpassing other proposed models’ performance. This study highlights the utility of combining game theory and machine learning in Ischemic stroke prediction and the potential of ensemble learning methods to increase predictive accuracy in ischemic stroke analysis.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_ee5383c15e728e57fc04861cfb3035c8 |
| identifier_str_mv | 10.1371/journal.pone.0328967.t003 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29903532 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Ranking of features for each algorithm.Pritam Chakraborty (9261302)Anjan Bandyopadhyay (19541099)Sricheta Parui (22049880)Sujata Swain (22049883)Partha Sarathy Banerjee (22049886)Tapas Si (12308990)Hong Qin (279614)Saurav Mallik (441729)Science PolicyBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedpreference algorithms identifyischemic stroke predictionincluding logistic regression39 %, surpassingsupport vector machineensemble learning methodsmachine learning techniquesintegrating machine learningaccurate predictions usingincrease predictive accuracycombining game theoryshapley value methodmachine learninggame theoryshapley valuepredictive capabilitiesmodels usingimpressive accuracyshapely valuetop 3study investigatesstudy highlightsresearch demonstratesperforming modelsnearest neighborimportant featuresdecision tree5 features<div><p>Stroke analysis using game theory and machine learning techniques. The study investigates the use of the Shapley value in predictive ischemic brain stroke analysis. Initially, preference algorithms identify the most important features in various machine learning models, including logistic regression, K-nearest neighbor, decision tree, support vector machine (linear kernel), support vector machine ( RBF kernel), neural networks, etc. For each sample, the top 3, 4, and 5 features are evaluated and selected to evaluate their performance. The Shapley value method was used to rank the models using their best four features based on their predictive capabilities. As a result, better-performing models were found. Afterward, ensemble machine learning methods were used to find the most accurate predictions using the top 5 models ranked by shapely value. The research demonstrates an impressive accuracy of 92.39%, surpassing other proposed models’ performance. This study highlights the utility of combining game theory and machine learning in Ischemic stroke prediction and the potential of ensemble learning methods to increase predictive accuracy in ischemic stroke analysis.</p></div>2025-08-13T19:10:38ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0328967.t003https://figshare.com/articles/dataset/Ranking_of_features_for_each_algorithm_/29903532CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/299035322025-08-13T19:10:38Z |
| spellingShingle | Ranking of features for each algorithm. Pritam Chakraborty (9261302) Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified preference algorithms identify ischemic stroke prediction including logistic regression 39 %, surpassing support vector machine ensemble learning methods machine learning techniques integrating machine learning accurate predictions using increase predictive accuracy combining game theory shapley value method machine learning game theory shapley value predictive capabilities models using impressive accuracy shapely value top 3 study investigates study highlights research demonstrates performing models nearest neighbor important features decision tree 5 features |
| status_str | publishedVersion |
| title | Ranking of features for each algorithm. |
| title_full | Ranking of features for each algorithm. |
| title_fullStr | Ranking of features for each algorithm. |
| title_full_unstemmed | Ranking of features for each algorithm. |
| title_short | Ranking of features for each algorithm. |
| title_sort | Ranking of features for each algorithm. |
| topic | Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified preference algorithms identify ischemic stroke prediction including logistic regression 39 %, surpassing support vector machine ensemble learning methods machine learning techniques integrating machine learning accurate predictions using increase predictive accuracy combining game theory shapley value method machine learning game theory shapley value predictive capabilities models using impressive accuracy shapely value top 3 study investigates study highlights research demonstrates performing models nearest neighbor important features decision tree 5 features |