Our study aims to find susceptibility genes from peripheral blood cells as potential molecular biomarkers of coronary heart disease (CHD) and to create a diagnosis model using bioinformatics combined with random forest (RF) and artificial neural network (ANN) .

We downloaded three gene expression profiles (GSE20680, GSE20681, GSE12288) from Gene Expression Omnibus (GEO) database. Then we performed analyses of differential expression, gene ontology terms, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways based on GSE20680. Next, the RF was used further to obtain the key genes from the differentially expressed genes. Finally, we set up a training set to construct the diagnostic model using ANN and two test sets to verify the diagnostic efficacy of the model by comprehensively merging the three datasets.

Using gene expression profiles in the GEO database, we identified 21 key genes from 284 differentially expressed genes by RF, and a new diagnostic model of CHD was also successfully constructed by using ANN to calculate the weight of key genes. Finally, two test sets were used to verify the diagnostic model's performance, and the AUC values were high (0.9024 and 0.8153 respectively) .

We identified 21 potential gene biomarkers of CHD and established a novel diagnostic model which shows a good result in the classification of CHD, and it may be helpful to CHD screening and early clinical diagnosis.