Research Paper Volume 12, Issue 24 pp 26221—26235
Construction and analysis of a joint diagnosis model of random forest and artificial neural network for heart failure
- 1 Peking University Fifth School of Clinical Medicine, Beijing Hospital, Beijing 100730, P.R. China
- 2 Department of Cardiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Science, Beijing 100730, P.R. China
Received: July 13, 2020 Accepted: September 29, 2020 Published: December 26, 2020https://doi.org/10.18632/aging.202405
How to Cite
Copyright: © 2020 Tian et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Heart failure is a global health problem that affects approximately 26 million people worldwide. As conventional diagnostic techniques for heart failure have been in practice with various limitations, it is necessary to develop novel diagnostic models to supplement existing methods. With advances and improvements in gene sequencing technology in recent years, more heart failure-related genes have been identified. Using existing gene expression data in the Gene Expression Omnibus (GEO) database, we screened differentially expressed genes (DEGs) of heart failure and identified six key genes (HMOX2, SERPINA3, LCN6, CSDC2, FREM1, and ZMAT1) by random forest classifier. Of these genes, CSDC2, FREM1, and ZMAT1 have never been associated with heart failure. We also successfully constructed a new diagnostic model of heart failure using an artificial neural network and verified its diagnostic efficacy in public datasets.