Abstract:
Purpose This study utilized data mining and machine learning (ML) techniques to identify new patterns and classifications
of the associations between nutrient intake and anemia among university students.
Methods We employed K-means clustering analysis algorithm and Decision Tree (DT) technique to identify the association
between anemia and vitamin and mineral intakes. We normalized and balanced the data based on anemia weighted clusters
for improving ML models’ accuracy. In addition, t-tests and Analysis of Variance (ANOVA) were performed to identify
significant differences between the clusters. We evaluated the models on a balanced dataset of 755 female participants from
the Hebron district in Palestine.
Results Our study found that 34.8% of the participants were anemic. The intake of various micronutrients (i.e., folate, Vit A,
B5, B6, B12, C, E, Ca, Fe, and Mg) was below RDA/AI values, which indicated an overall unbalanced malnutrition in the
present cohort. Anemia was significantly associated with intakes of energy, protein, fat, Vit B1, B5, B6, C, Mg, Cu and Zn.
On the other hand, intakes of protein, Vit B2, B5, B6, C, E, choline, folate, phosphorus, Mn and Zn were significantly lower
in anemic than in non-anemic subjects. DT classification models for vitamins and minerals (accuracy rate: 82.1%) identified
an inverse association between intakes of Vit B2, B3, B5, B6, B12, E, folate, Zn, Mg, Fe and Mn and prevalence of anemia.
Conclusions Besides the nutrients commonly known to be linked to anemia—like folate, Vit B6, C, B12, or Fe—the cluster
analyses in the present cohort of young female university students have also found choline, Vit E, B2, Zn, Mg, Mn, and
phosphorus as additional nutrients that might relate to the development of anemia. Further research is needed to elucidate
if the intake of these nutrients might influence the risk of anemia.
