Predicting nutritional status for women of childbearing age from their economic, health, and demographic features: A supervised machine learning approach

• Md. Mohsan Khudri,

  Roles Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing

  Affiliation Department of Economics, Fogelman College of Business and Economics, The University of Memphis, Memphis, Tennessee, United States of America

  https://orcid.org/0000-0003-4960-2259

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• Kang Keun Rhee,

  Roles Data curation, Formal analysis, Methodology, Writing – original draft

  Affiliation Department of Economics, Fogelman College of Business and Economics, The University of Memphis, Memphis, Tennessee, United States of America

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• Mohammad Shabbir Hasan ,

  Contributed equally to this work with: Mohammad Shabbir Hasan, Karar Zunaid Ahsan

  Roles Formal analysis, Visualization, Writing – review & editing

  Affiliation Department of Computer Science, Virginia Tech, Blacksburg, VA, United States of America

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• Karar Zunaid Ahsan

  Contributed equally to this work with: Mohammad Shabbir Hasan, Karar Zunaid Ahsan

  Roles Methodology, Supervision, Writing – original draft, Writing – review & editing

  * E-mail: zunaid@email.unc.edu

  Affiliation Public Health Leadership Program, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America

  https://orcid.org/0000-0001-7630-288X

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