Enhancing BMI-Based Student Clustering by Considering Fitness as Key Attribute
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.
KeywordsImproving BMI-based classification Fitness-based clustering Multiobjective problem
- 3.Eurofit: Eurofit Tests of Physical Fitness. Council of Europe, Strasbourg, 2 edn. (1993)Google Scholar
- 5.Kallioinen, M., Granheim, S.I.: Overweight and obesity in the western pacific region. Technical report, World Health Organization (2017)Google Scholar
- 11.Zhang, T., Ramakrishnan, R., Livny, M.: Birch: an efficient data clustering method for very large databases. In: Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, pp. 103–114 (1996)Google Scholar