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Training Neural networks for Experimental models: Classifying Biomedical Datasets for Sickle Cell Disease

Khalaf, M, Hussain, A, Al-Jumeily, D, Keight, R, Keenan, R, Fergus, P, AlAskar, H, Shaw, A and Olatunji, I (2016) Training Neural networks for Experimental models: Classifying Biomedical Datasets for Sickle Cell Disease. In: Intelligent Computing Theories and Application: Lecture Notes in Computer Science , 9771. pp. 784-795. (2016 International Conference on Intelligent Computation, 02 August 2016 - 05 August 2016, Lanzhou,China).

Mohammed Khalaf Lecture notes in Computer science (ICIC Paper 2016).pdf - Accepted Version

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This paper presents 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 patents. 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 ROC 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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-42291-6_78
Uncontrolled Keywords: 08 Information And Computing Sciences
Subjects: Q Science > QA Mathematics > QA76 Computer software
R Medicine > RM Therapeutics. Pharmacology
Divisions: Civil Engineering & Built Environment
Computer Science & Mathematics
Publisher: Springer Verlag (Germany)
Date Deposited: 16 Dec 2016 11:34
Last Modified: 13 Apr 2022 15:14
URI: https://researchonline.ljmu.ac.uk/id/eprint/3546
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