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. 2021 Mar 27:2021:5569039.
doi: 10.1155/2021/5569039. eCollection 2021.

Clinical Influencing Factors of Acute Myocardial Infarction Based on Improved Machine Learning

Hongwei Du  1 , Linxing Feng  1 , Yan Xu  1 , Enbo Zhan  1 , Wei Xu  1
Affiliations

Clinical Influencing Factors of Acute Myocardial Infarction Based on Improved Machine Learning

Hongwei Du et al. J Healthc Eng. .

Retraction in

Abstract

At present, there is no method to predict or monitor patients with AMI, and there is no specific treatment method. In order to improve the analysis of clinical influencing factors of acute myocardial infarction, based on the machine learning algorithm, this paper uses the K-means algorithm to carry out multifactor analysis and constructs a hybrid model combined with the ART2 network. Moreover, this paper simulates and analyzes the model training process and builds a system structure model based on the KNN algorithm. After constructing the model system, this paper studies the clinical influencing factors of acute myocardial infarction and combines mathematical statistics and factor analysis to carry out statistical analysis of test results. The research results show that the system model constructed in this paper has a certain effect in the clinical analysis of acute myocardial infarction.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Local minimum and global optimal.
Figure 2
Figure 2
Typical ART2 network structure.
Figure 3
Figure 3
Algorithm simulation diagram.
Figure 4
Figure 4
KNN model.
Figure 5
Figure 5
Clustering effect 1.
Figure 6
Figure 6
Clustering effect 2.
Figure 7
Figure 7
Integrated model structure.
Figure 8
Figure 8
Classifier model structure.
Figure 9
Figure 9
Flow chart of data test.
Figure 10
Figure 10
Statistical diagram of test results of model data.
Figure 11
Figure 11
Statistical diagram of model single factor processing.
Figure 12
Figure 12
Statistical diagram of model multifactor processing.
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