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Research article
Yan He
First Affiliated Hospital of Guangxi Medical University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-9913-1275
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This work is licensed under a CC BY 4.0 License. Read Full License
An artificial neural network (ANN) model was developed to predict the risks of complicating ventricular tachyarrhythmia (VTA) in patients with acute myocardial infarction (AMI). We enrolled information of 503 patients with 13 risk factors from the affiliated hospital of Guangdong medical university from January 2017 to December 2019. Risk factors were dimensionally reduced and simplified as new variables by principal component analysis (PCA). The cohort were randomly divided into a training set and a testing set at the ratio of 70%:30%. Training set was used to develop a model for the prediction of VTA while testing set was used to evaluate the performance of the model. Three new comprehensive variables by PCA are able to reflect all information of the original data. We determined the prediction model with optimizing parameters by cyclic searching which includes an input layer of three comprehensive variables, a single hidden layer composed of two neurons and a output layer. The area under curve (AUC) is 0.812 in training set and confusion matrix with accuracy 94.60%, sensitivity 63.04%, specificity 99.35%, positive predicative value 93.55%, negative predictive value 94.70%. The model displayed a decreased but medium discrimination with an AUC of 0.688 in the independent testing cohort, confusion matrix with accuracy 87.42%, Sensitivity 39.26%, specificity 98.37%, positive predicative value 84.62%, negative predictive value 87.68%. The research suggests that ANN model could be used to predict the risk of complicating ventricular tachyarrhythmia after acute myocardial infarction while should be further improved.
Keywords
artificial neural network, ventricular tachyarrhythmia, acute myocardial infarction
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This is a preprint. It has not completed peer review.
Research article
Yan He
First Affiliated Hospital of Guangxi Medical University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-9913-1275
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 12 Aug, 2020
No community comments so far
Subject Areas
Medical Informatics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
An artificial neural network (ANN) model was developed to predict the risks of complicating ventricular tachyarrhythmia (VTA) in patients with acute myocardial infarction (AMI). We enrolled information of 503 patients with 13 risk factors from the affiliated hospital of Guangdong medical university from January 2017 to December 2019. Risk factors were dimensionally reduced and simplified as new variables by principal component analysis (PCA). The cohort were randomly divided into a training set and a testing set at the ratio of 70%:30%. Training set was used to develop a model for the prediction of VTA while testing set was used to evaluate the performance of the model. Three new comprehensive variables by PCA are able to reflect all information of the original data. We determined the prediction model with optimizing parameters by cyclic searching which includes an input layer of three comprehensive variables, a single hidden layer composed of two neurons and a output layer. The area under curve (AUC) is 0.812 in training set and confusion matrix with accuracy 94.60%, sensitivity 63.04%, specificity 99.35%, positive predicative value 93.55%, negative predictive value 94.70%. The model displayed a decreased but medium discrimination with an AUC of 0.688 in the independent testing cohort, confusion matrix with accuracy 87.42%, Sensitivity 39.26%, specificity 98.37%, positive predicative value 84.62%, negative predictive value 87.68%. The research suggests that ANN model could be used to predict the risk of complicating ventricular tachyarrhythmia after acute myocardial infarction while should be further improved.
Figure 1
Figure 2
Figure 3
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