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Training Neural Networks as Experimental Models: Classifying Biomedical Datasets for Sickle Cell Disease

  • Mohammed KhalafEmail author
  • Abir Jaafar Hussain
  • Dhiya Al-Jumeily
  • Robert Keight
  • Russell Keenan
  • Paul Fergus
  • Haya Al-Askar
  • Andy Shaw
  • Ibrahim Olatunji Idowu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9771)

Abstract

This paper discusses the use of various type of neural network architectures for the classification of medical data. Extensive research has indicated that neural networks generate significant improvements when used for the pre-processing of medical time-series data signals and have assisted in obtaining high accuracy in the classification of medical data. Up to date, most of hospitals and healthcare sectors in the United Kingdom are using manual approach for analysing patient input for sickle cell disease, which depends on clinician’s experience that can lead to time consuming and stress to patients. The results obtained from a range of models during our experiments have shown that the proposed Back-propagation trained feed-forward neural network classifier generated significantly better outcomes over the other range of classifiers. Using the Receiver Operating Characteristic curve, experiments results showed the following outcomes for our models, in order of best to worst: Back-propagation trained feed-forward neural net classifier: 0.989, Functional Link Neural Network: 0.972, in comparison to the Radial basis neural Network Classifiers with areas of 0.875, and the Voted Perception classifier: 0.766. A Linear Neural Network was used as baseline classifier to illustrate the importance of the previous models, producing an area of 0.849, followed by a random guessing model with an area of 0.524.

Keywords

Neural network architectures Sickle cell disease Real datasets The area under curve Receiver operating characteristic curve e-Health 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Mohammed Khalaf
    • 1
    Email author
  • Abir Jaafar Hussain
    • 1
  • Dhiya Al-Jumeily
    • 1
  • Robert Keight
    • 1
  • Russell Keenan
    • 2
  • Paul Fergus
    • 1
  • Haya Al-Askar
    • 3
  • Andy Shaw
    • 1
  • Ibrahim Olatunji Idowu
    • 1
  1. 1.Faculty of Engineering and TechnologyLiverpool John Moores UniversityLiverpoolUK
  2. 2.Liverpool Paediatric Haemophilia Centre, Haematology Treatment CentreAlder Hey Children’s HospitalLiverpoolUK
  3. 3.College of Computer Engineering and ScienceSattam Bin Abdulaziz UniversityAl-KharjKingdom of Saudi Arabia

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