目的:这项研究利用数据挖掘和机器学习(ML)技术来确定大学生营养摄入与贫血之间关联的新模式和分类。
方法:我们采用K均值聚类分析算法和决策树(DT)技术来确定贫血与维生素和矿物质摄入量之间的关联。我们基于贫血加权聚类对数据进行了归一化和平衡,以提高ML模型的准确性。此外,进行t检验和方差分析(ANOVA)以鉴定聚类之间的显著差异。我们在来自巴勒斯坦希伯伦地区的755名女性参与者的平衡数据集上评估了模型。
结果:我们的研究发现34.8%的参与者贫血。各种微量营养素的摄入量(即,叶酸,维生素A,B5,B6,B12,C,E,Ca,Fe,和Mg)低于RDA/AI值,这表明本队列总体营养不良不平衡。贫血与能量摄入显著相关,蛋白质,脂肪,维生素B1、B5、B6、C、Mg,Cu和Zn。另一方面,蛋白质的摄入量,维生素B2,B5,B6,C,E,胆碱,叶酸,磷,贫血受试者的Mn和Zn显着低于非贫血受试者。维生素和矿物质的DT分类模型(准确率:82.1%)确定了维生素B2,B3,B5,B6,B12,E,叶酸,Zn,Mg,铁和锰与贫血的患病率。
结论:除了通常已知与贫血样叶酸有关的营养素,维生素B6,C,B12或Fe-目前年轻女大学生队列中的聚类分析也发现了胆碱,VitE,B2,Zn,Mg,Mn,和磷作为额外的营养素,可能与贫血的发展有关。需要进一步的研究来阐明这些营养素的摄入是否会影响贫血的风险。
OBJECTIVE: 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.