Development and validation of a diagnostic model for ischemic cardiomyopathy with Artificial Neural Network by bioinformatic analysis

Abstract Background Ischemic cardiomyopathy(ICM) is a significant global health concern caused by high morbidity and mortality.In addition, no previous study has reported the diagnostic biomarkers in ICM. Objective The presentstudy is aimed at establishing and validating a diagnostic model for ICM with Artificial Neural Network (ANN) by screening key potential biomarkers using bioinformatic analysis. Method Through searching the Gene Expression Omnibus(GEO) database, three gene expression datasets were downloaded and merged. Differentially expressed genes(DEGs) in the mergeddatasetswere detectedusing R software and subject to Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment analysis. Then, Lasso regression analysis and random forest (RF) wereapplied to identify critical genes based on DEGs. Afterwards, we intersected the key genes screened from Lasso regression analysis and RF. An ICM diagnostic model was constructed by ANN. Based on a validation dataset, the diagnostic model was assessed, whereasits diagnostic performance was assessed usingarea under curve(AUC) values. Results Totally 18 ICM-related DEGs were detected. Then, six hub genes(COL1A1, FCN3, GLUL, MYOT, SERPINA3, and SLC38A2) were identified by intersecting the key genes filtered out by Lasso regression analysis and Random forest(RF). In the end, a diagnostic model for ICM was successfully designed by ANN, obtaining an AUC of 0.907 and 0.745 in training datasets, separately. Conclusion this study detected several potential genetic biomarkers and successfully developed an early predictive model with high diagnostic performance for ICM. In addition, the obtained findings offer a significant guidance for the early diagnosis as well as screening of ICM in the future.