Enhancing stroke disease classification through machine learning models via a novel voting system by feature selection techniques

• Mahade Hasan ,

  Contributed equally to this work with: Mahade Hasan, Farhana Yasmin, Md. Mehedi Hassan

  Roles Data curation, Formal analysis, Resources, Writing – review & editing

  Affiliation School of Software, Nanjing University of Information Science and Technology, Nanjing, China

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• Farhana Yasmin ,

  Contributed equally to this work with: Mahade Hasan, Farhana Yasmin, Md. Mehedi Hassan

  Roles Formal analysis, Resources, Validation, Writing – original draft

  Affiliation Department of Computer Science and Technology, Nanjing University of Information Science and Technology, Nanjing, China

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• Md. Mehedi Hassan ,

  Contributed equally to this work with: Mahade Hasan, Farhana Yasmin, Md. Mehedi Hassan

  Roles Conceptualization, Investigation, Methodology, Project administration, Writing – original draft

  * E-mail: mehedihassan@ieee.org

  Affiliation Computer Science and Engineering Discipline, Khulna University, Khulna, Bangladesh

  https://orcid.org/0000-0002-9890-0968

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• Xue Yu,

  Roles Resources, Writing – review & editing

  Affiliation School of Software, Nanjing University of Information Science and Technology, Nanjing, China

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• Soniya Yeasmin,

  Roles Software, Validation

  Affiliation Department of Computer Science and Engineering, North Western University, Khulna, Bangladesh

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• Herat Joshi,

  Roles Writing – review & editing

  Affiliation Great River Health Systems, Burlington, IA, United States of America

  https://orcid.org/0009-0009-4199-544X

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• Sheikh Mohammed Shariful Islam

  Roles Methodology, Writing – review & editing

  Affiliation Institute for Physical Activity and Nutrition, Deakin University, Melbourne, VIC, Australia

  https://orcid.org/0000-0001-7926-9368

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