AUTHOR=Wu Yanze , Chen Hui , Li Lei , Zhang Liuping , Dai Kai , Wen Tong , Peng Jingtian , Peng Xiaoping , Zheng Zeqi , Jiang Ting , Xiong Wenjun 

TITLE=Construction of Novel Gene Signature-Based Predictive Model for the Diagnosis of Acute Myocardial Infarction by Combining Random Forest With Artificial Neural Network

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2022.876543

DOI=10.3389/fcvm.2022.876543

ISSN=2297-055X

ABSTRACT=Background: Acute myocardial infarction (AMI) is one of the most common causes of mortality around the world. Early diagnosis of AMI contributes to improving prognosis. In our study, we aimed to construct a novel predictive model for the diagnosis of AMI using an artificial neural network, and we verified its diagnostic value via constructing ROC.
Methods: We downloaded three publicly available datasets (training sets GSE48060, GSE60993, GSE66360) from Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified between 87 AMI and 78 control samples. We applied the random forest (RF) and artificial neural network (ANN) algorithms to further identify novel gene signatures and constructed a model to predict the possibility of AMI. Besides, the diagnostic value of our model was further validated in the validation set GSE61144 (7 AMI patients and 10 controls).
Results: A total of 71 DEGs were identified, of which 68 were upregulated and 3 were downregulated. Firstly, 11 key genes in 71 DEGs were screened with RF classifier for classification of AMI and control samples. Then, we calculated the weight of each key gene using ANN. Furthermore, the diagnostic model was constructed and named neuralAMI, with a significantly predictive power (AUC = 0.988). Finally, our model was validated with an independent dataset GSE61144 (AUC = 0.743).
Conclusions: Machine learning was used to develop a reliable predictive model for the diagnosis of AMI. The results in our study provides potential gene biomarkers for early disease screening.