Machine learning-based prediction of survival prognosis in cervical cancer.

ABSTRACT:

Background

Accurately forecasting the prognosis could improve cervical cancer management, however, the currently used clinical features are difficult to provide enough information. The aim of this study is to improve forecasting capability by developing a miRNAs-based machine learning survival prediction model.

Results

The expression characteristics of miRNAs were chosen as features for model development. The cervical cancer miRNA expression data was obtained from The Cancer Genome Atlas database. Preprocessing, including unquantified data removal, missing value imputation, samples normalization, log transformation, and feature scaling, was performed. In total, 42 survival-related miRNAs were identified by Cox Proportional-Hazards analysis. The patients were optimally cluste

SUBMITTER: Ding D 

PROVIDER: S-EPMC8207793 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature