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21 July 2023 Cascade models of neural networks and machine learning algorithms for heart disease classification
Yi Yang
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Proceedings Volume 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 127170X (2023) https://doi.org/10.1117/12.2684785
Event: 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 2023, Wuhan, China
Abstract
Artificial Neural Networks (ANN) are now widely used in the medical field. However, on many datasets, only using ANN does not necessarily yield high prediction accuracy to some extent. Therefore, in this paper, the authors tried combinations of different models to obtain better accuracy based on the binary classification of a heart failure dataset collected from Pakistan, Faisalabad hospital. Briefly, the author considered ANN as feature extractors and then connected some statistical binary classifiers to build models, such as ANN with Support Vector Machines, ANN with Logistic Regression, and ANN with Random Forest. In particular, the authors used two different kernels of Support Vector Machines to build models in the artificial neural network with Support Vector Machines. One kernel is the radial basis function kernel; the other is the polynomial kernel. Experimental results indicate that models combining ANN and other statistical binary classifiers all get better performance than the ANN model alone. Moreover, the model with the highest accuracy is the ANN combined with Random Forest, followed by the ANN combined with Support Vector Machines and Logistic Regression.
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Yi Yang "Cascade models of neural networks and machine learning algorithms for heart disease classification", Proc. SPIE 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 127170X (21 July 2023); https://doi.org/10.1117/12.2684785
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KEYWORDS
Artificial neural networks
Random forests
Machine learning
Support vector machines
Education and training
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