TY - JOUR

T1 - Identification and prediction of association patterns between nutrient intake and anemia using machine learning techniques

T2 - results from a cross-sectional study with university female students from Palestine

AU - Qasrawi, Radwan

AU - Badrasawi, Manal

AU - Al-Halawa, Diala Abu

AU - Polo, Stephanny Vicuna

AU - Khader, Rami Abu

AU - Al-Taweel, Haneen

AU - Alwafa, Reem Abu

AU - Zahdeh, Rana

AU - Hahn, Andreas

AU - Schuchardt, Jan Philipp

N1 - Publisher Copyright: © The Author(s) 2024.

PY - 2024/8

Y1 - 2024/8

N2 - 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.

AB - 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.

KW - Classification and regression tree

KW - Dietary patterns

KW - Iron deficiency anemia

KW - K-means analysis

KW - Machine learning

KW - Nutrient intake

UR - http://www.scopus.com/inward/record.url?scp=85188445706&partnerID=8YFLogxK

U2 - 10.1007/s00394-024-03360-8

DO - 10.1007/s00394-024-03360-8

M3 - Article

C2 - 38512358

VL - 63

SP - 1635

EP - 1649

JO - European journal of nutrition

JF - European journal of nutrition

SN - 1436-6207

IS - 5

ER -