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Classifying Leukemia and Gout Patients with Neural Networks

  • Guryash Bahra
  • Lena Wiese
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 903)

Abstract

Machine Learning is one of the top growing fields of recent times and is applied in various areas such as healthcare. In this article, machine learning is used to study the patients suffering from either gout or leukemia, but not both, with the use of their uric acid signatures. The study of the uric acid signatures involves the application of supervised machine learning, using an artificial neural network (ANN) with one hidden layer and sigmoid activation function, to classify patients and the calculation of the accuracy with k-fold cross validation. We identify the number of nodes in the hidden layer and a value for the weight decay parameter that are optimal in terms of accuracy and ensure good performance.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Institute of Computer ScienceUniversity of GöttingenGöttingenGermany

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