Parameterizing neural networks for disease classification

2019 | journal article. A publication with affiliation to the University of Göttingen.

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​Parameterizing neural networks for disease classification​
Bahra, G. & Wiese, L. ​ (2019) 
Expert Systems37(1) art. e12465​.​ DOI: https://doi.org/10.1111/exsy.12465 

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Authors
Bahra, Guryash; Wiese, Lena 
Abstract
Neural networks are one option to implement decision support systems for health care applications. In this paper, we identify optimal settings of neural networks for medical diagnoses: The study involves the application of supervised machine learning using an artificial neural network to distinguish between gout and leukaemia patients. With the objective to improve the base accuracy (calculated from the initial set‐up of the neural network model), several enhancements are analysed, such as the use of hyperbolic tangent activation function instead of the sigmoid function, the use of two hidden layers instead of one, and transforming the measurements with linear regression to obtain a smoothened data set. Another setting we study is the impact on the accuracy when using a data set of reduced size but with higher data quality. We also discuss the tradeoff between accuracy and runtime efficiency.
Issue Date
2019
Journal
Expert Systems 
ISSN
0266-4720
eISSN
1468-0394
Language
English

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