A comparison of different machine-learning techniques for the selection of a panel of metabolites allowing early detection of brain tumors.

ABSTRACT: Metabolomics combined with machine learning methods (MLMs), is a powerful tool for searching novel diagnostic panels. This study was intended to use targeted plasma metabolomics and advanced MLMs to develop strategies for diagnosing brain tumors. Measurement of 188 metabolites was performed on plasma samples collected from 95 patients with gliomas (grade I-IV), 70 with meningioma, and 71 healthy individuals as a control group. Four predictive models to diagnose glioma were prepared using 10 MLMs and a conventional approach. Based on the cross-validation results of the created models, the F1-scores were calculated, then obtained values were compared. Subsequently, the best algorithm was applied to perform five comparisons involving gliomas, meningiomas, and controls. The best results were o

SUBMITTER: Godlewski A 

PROVIDER: S-EPMC10329700 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature