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.
This work is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 727560.
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Dovgan, E., Leskošek, B., Jurak, G., Starc, G., Sorić, M., Luštrek, M. (2019). Enhancing BMI-Based Student Clustering by Considering Fitness as Key Attribute. In: Kralj Novak, P., Šmuc, T., Džeroski, S. (eds) Discovery Science. DS 2019. Lecture Notes in Computer Science(), vol 11828. Springer, Cham. https://doi.org/10.1007/978-3-030-33778-0_13
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