title: Application of boosting classification and regression to modeling the relationships between trace elements and diseases.
abstract: The study on the relationship between trace elements and diseases often need to build a classification/regression model. Furthermore, the accuracy of such a model is of particular importance and directly decides its applicability. The goal of this study is to explore the feasibility of applying boosting, i.e., a new strategy from machine learning, to model the relationship between trace elements and diseases. Two examples are employed to illustrate the technique in the applications of classification and regression, respectively. The first example involves the diagnosis of anorexia according to the concentrations of six elements (i.e. classification task). Decision stump and support vector machine are used as the weak/base algorithm and reference algorithm, respectively. The second example involves the prediction of breast cancer mortality based on the intake of trace elements (i.e. a regression task). In this regard, partial least squares is not only used as the weak/base algorithm, but also the reference algorithm. The results from both examples confirm the potential of boosting in modeling the relationship between trace elements and diseases.
authors.affiliation: Department of Chemistry and Chemical Engineering, Yibin University, Yibin 644007, People's Republic of China. chaotan1112@163.com
authors.forename: Chao
authors.initials: C
authors.lastname: Tan
authors.forename: Hui
authors.initials: H
authors.lastname: Chen
authors.forename: Wanping
authors.initials: W
authors.lastname: Zhu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000378:metabolism; D002965:Classification; D005260:Female; D006801:Humans; D015233:Models, Statistical; D012044:Regression Analysis; D014131:Trace Elements / Q000145:classification / Q000378:metabolism*
chemical_list: D014131:Trace Elements