CNN_SVM-based myocardial infarction disease prediction

Manfu Ma; Xuan Sun; Yong Li; Di Wang

doi:10.1117/12.2687452

28 August 2023 CNN_SVM-based myocardial infarction disease prediction

Manfu Ma, Xuan Sun, Yong Li, Di Wang

Proceedings Volume 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023); 127241P (2023) https://doi.org/10.1117/12.2687452
Event: Second International Conference on Biomedical and Intelligent Systems (IC-BIS2023), 2023, Xiamen, China

Abstract

Cardiovascular diseases are the most common type of diseases with the highest mortality rate, and the most common of these diseases is myocardial infarction. Regarding the prediction of myocardial infarction, the traditional method is judged by the expertise and experience of medical personnel, but this method may lead to inaccurate results due to differences in judgment criteria, while using deep learning methods to study myocardial infarction facilitates the processing of features and can improve the accuracy of diagnosing the disease. In this paper, we propose a CNN_SVM model of convolutional neural network combined with support vector machine to predict myocardial infarction. A total of 87 features are obtained based on the vital signs and biochemical examinations of patients in the MIMIC data set. For these features, the CNN_SVM model firstly performs PCA dimensionality reduction by mapping the data from high dimension to low dimension to reduce the dimensionality of the features and retain as much information as possible to complete the dimensionality reduction, and finally obtains 45 data features; secondly, the last layer of the CNN model, Softmax classification layer, is replaced with SVM classification, and the final results are two kinds, namely myocardial infarction and non-myocardial infarction, respectively. The model construction was completed. The experiments showed that the accuracy of CNN_SVM model was 96.67%: the accuracy was improved by 7.60%, 7.15% and 17% when compared with CNN, XGBoost and MLP, respectively.

Citation Download Citation

Manfu Ma, Xuan Sun, Yong Li, and Di Wang "CNN_SVM-based myocardial infarction disease prediction", Proc. SPIE 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023), 127241P (28 August 2023); https://doi.org/10.1117/12.2687452

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KEYWORDS

Cardiovascular disorders

Machine learning

Heart

Deep learning

Neurological disorders

Convolutional neural networks

Vascular diseases

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