Heart Disease Classification Using Artificial Neural Networks

Rati Wongsathan; Pasit Pothong

doi:10.4028/www.scientific.net/AMM.781.624

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 781Heart Disease Classification Using Artificial...

Heart Disease Classification Using Artificial Neural Networks

Abstract:

Neural Networks (NNs) has emerged as an importance tool for classification in the field of decision making. The main objective of this work is to design the structure and select the optimized parameter in the neural networks to implement the heart disease classifier. Three types of neural networks, i.e. Multi-layered Perceptron Neural Network (MLP-NN), Radial Basis Function Neural Networks (RBF-NN), and Generalized Regression Neural Network (GR-NN) have been used to test the performance of heart disease classification. The classification accuracy obtained by RBFNN gave a very high performance than MLP-NN and GR-NN respectively. The performance of accuracy is very promising compared with the previously reported another type of neural networks.

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Periodical:

Applied Mechanics and Materials (Volume 781)

Pages:

624-627

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.781.624

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Online since:

August 2015

Authors:

Rati Wongsathan*, Pasit Pothong

Keywords:

Generalized Regression Neural Networks, Heart Disease Classification, Multi-Layered Perceptron Neural Networks, Radial Basis Function Neural Networks

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* - Corresponding Author

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