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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

Yuqing Tian1,2, Jiefu Yang2, Ming Lan2, Tong Zou1,2
  • 1Peking University Fifth School of Clinical Medicine, Beijing 100730, P.R. China
  • 2Department of Cardiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Science, Beijing 100730, P.R. China
* Equal contribution
Received: July 13, 2020Accepted: September 29, 2020Published: December 26, 2020
https://doi.org/10.18632/aging.202405

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.

Abstract

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.