Identification of differentially expressed metabolites via RF and SVM Algorithms
sepsis
SEPSIS
In this study, various machine learning algorithms were employed to identify differentially expressed metabolites (DEMs). The random forest (RF) algorithm was utilized to determine the optimum DEMs between the sepsis and healthy control groups. Using the "e1071" R package, the support vector machine (SVM) generated eigenvectors were eliminated to extract the optimal variables for identifying diagnostic DEMs in sepsis. Additionally, a SVM classifier was constructed with tenfold cross-validation. Finally, a Venn diagram was employed to identify the shared DEMs.