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Classifying Diabetes Disease Using Feedforward MLP Neural Networks

Classifying Diabetes Disease Using Feedforward MLP Neural Networks

Ahmad Al-Khasawneh, Haneen Hijazi
Copyright: © 2022 |Pages: 24
ISBN13: 9781668424087|ISBN10: 1668424088|EISBN13: 9781668424094
DOI: 10.4018/978-1-6684-2408-7.ch061
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MLA

Al-Khasawneh, Ahmad, and Haneen Hijazi. "Classifying Diabetes Disease Using Feedforward MLP Neural Networks." Research Anthology on Artificial Neural Network Applications, edited by Information Resources Management Association, IGI Global, 2022, pp. 1263-1286. https://doi.org/10.4018/978-1-6684-2408-7.ch061

APA

Al-Khasawneh, A. & Hijazi, H. (2022). Classifying Diabetes Disease Using Feedforward MLP Neural Networks. In I. Management Association (Ed.), Research Anthology on Artificial Neural Network Applications (pp. 1263-1286). IGI Global. https://doi.org/10.4018/978-1-6684-2408-7.ch061

Chicago

Al-Khasawneh, Ahmad, and Haneen Hijazi. "Classifying Diabetes Disease Using Feedforward MLP Neural Networks." In Research Anthology on Artificial Neural Network Applications, edited by Information Resources Management Association, 1263-1286. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-6684-2408-7.ch061

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Abstract

Diagnosing chronic diseases is about making accurate and quick decisions based on contradictory information and constantly evolving knowledge. Hence, there has been a persistent need to help health practitioners in making correct decisions. Diabetes is a common chronic disease. It is a global healthcare threat and the eighth leading cause of death in the world. Modern artificial intelligence techniques are being used in diagnosing chronic diseases including artificial neural networks. In this chapter, a feedforward multilayer-perceptron neural network has been implemented to help health practitioners in classifying diabetes. Through the work, an algorithm was proposed in purpose of determining the number of hidden layers and neurons in a MLP. Based on the algorithm, two topologies have been introduced. Both topologies exhibited good classification accuracies with a slightly higher accuracy for the MLP with only one hidden layer. The data set was obtained from King Abdullah University Hospital in Jordan.

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