Details
| Original language | English |
|---|---|
| Pages (from-to) | 1635-1649 |
| Number of pages | 15 |
| Journal | European journal of nutrition |
| Volume | 63 |
| Issue number | 5 |
| Early online date | 21 Mar 2024 |
| Publication status | Published - Aug 2024 |
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.
Keywords
- Classification and regression tree, Dietary patterns, Iron deficiency anemia, K-means analysis, Machine learning, Nutrient intake
ASJC Scopus subject areas
- Nursing(all)
- Nutrition and Dietetics
- Medicine(all)
- Medicine (miscellaneous)
Sustainable Development Goals
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In: European journal of nutrition, Vol. 63, No. 5, 08.2024, p. 1635-1649.
Research output: Contribution to journal › Article › Research › peer review
}
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 -