Construction and analysis of heart failure diagnosis model based on random forest and artificial neural network.

ABSTRACT: Heart failure is a global health problem and the number of sufferers is increasing as the population grows and ages. Existing diagnostic techniques for heart failure have various limitations in the clinical setting and there is a need to develop a new diagnostic model to complement the existing diagnostic methods. In recent years, with the development and improvement of gene sequencing technology, more genes associated with heart failure have been identified. We screened for differentially expressed genes in heart failure using available gene expression data from the Gene Expression Omnibus database and identified 6 important genes by a random forest classifier (ASPN, MXRA5, LUM, GLUL, CNN1, and SERPINA3). And we have successfully constructed a new heart failure diagnostic model using an a

SUBMITTER: Boyang C 

PROVIDER: S-EPMC9575800 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature