Segmentation of college students according to socioeconomic status, caloric intake and body composition using the K-means algorithm
DOI:
https://doi.org/10.55204/trc.v4i2.e433Keywords:
Cluster analysis, Eating behavior, Obesity, Nutritional study, Preventive strategieAbstract
The increase in health problems such as obesity and metabolic conditions has reinforced the need to understand how socioeconomic status (SES) impacts nutrition, especially among young adults. This study used the K-means algorithm to segment young adult college students based on their SES, caloric intake, and body mass index (BMI) values. Data were collected from 150 college students using validated questionnaires that measured the amount of calories consumed and the proportion of macronutrients. The analysis was carried out at univariate, bivariate and multivariate levels using the K-means algorithm, identifying three clusters within the study group; Python on the Google Colab platform was used to process the information. The results identified three clusters: one with a caloric intake slightly higher than recommended, but with a normal BMI, suggesting better diet quality or increased physical activity; another with a caloric intake lower than recommended and a BMI indicating overweight, possibly due to a low-quality diet; and a third cluster with high levels of energy intake, potentially including high-performance athletes. Dietary variability underscores the importance of personalizing nutritional strategies to improve health and well-being.
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