Predicting age at onset of childhood obesity using regression, Random Forest, Decision Tree, and K-Nearest Neighbour—A case study in Saudi Arabia

• Salem Hamoud Alanazi ,

  Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft

  * E-mail: s3755901@student.rmit.edu.au

  Affiliations School of Science, RMIT University, Melbourne, Victoria, Australia, Department of Mathematics, College of Sciences, Northern Border University, Arar, Saudi Arabia

  https://orcid.org/0009-0007-7126-2146

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• Mali Abdollahian ,

  Contributed equally to this work with: Mali Abdollahian, Laleh Tafakori

  Roles Conceptualization, Methodology, Project administration, Supervision, Writing – review & editing

  Affiliation School of Science, RMIT University, Melbourne, Victoria, Australia

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• Laleh Tafakori ,

  Contributed equally to this work with: Mali Abdollahian, Laleh Tafakori

  Roles Conceptualization, Methodology, Project administration, Supervision, Writing – review & editing

  Affiliation School of Science, RMIT University, Melbourne, Victoria, Australia

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• kheriah Ahmed Almulaihan ,

  Roles Investigation, Resources

  ¶‡ KAA, SMA and OFA also contributed equally to this work.

  Affiliation Maternity and Children Hospital, Arar, Saudi Arabia

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• Salman Mutarid ALruwili ,

  Roles Investigation, Resources

  ¶‡ KAA, SMA and OFA also contributed equally to this work.

  Affiliation North Medical Tower Hospital, Arar, Saudi Arabia

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• Omar Falleh ALenazi

  Roles Investigation, Resources

  ¶‡ KAA, SMA and OFA also contributed equally to this work.

  Affiliation North Medical Tower Hospital, Arar, Saudi Arabia

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