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Enhancing BMI-Based Student Clustering by Considering Fitness as Key Attribute

  • Erik DovganEmail author
  • Bojan Leskošek
  • Gregor Jurak
  • Gregor Starc
  • Maroje Sorić
  • Mitja Luštrek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11828)

Abstract

The purpose of this study was to redefine health and fitness categories of students, which were defined based on body mass index (BMI). BMI enables identifying overweight and obese persons, however, it inappropriately classifies overweight-and-fit and normal-weight-and-non-fit persons. Such a classification is required when personalized advice on healthy life style and exercises is provided to students. To overcome this issue, we introduced a clustering-based approach that takes into account a fitness score of students. This approach identifies fit and not-fit students, and in combination with BMI, students that are overweight-and-fit and those that are normal-weight-and-non-fit. These results enable us to better target students with personalized advice based on their actual physical characteristics.

Keywords

Improving BMI-based classification Fitness-based clustering Multiobjective problem 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Intelligent SystemsJožef Stefan InstituteLjubljanaSlovenia
  2. 2.Faculty of SportUniversity of LjubljanaLjubljanaSlovenia

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