TY - GEN
T1 - Training neural networks as experimental models
T2 - 12th International Conference on Intelligent Computing Theories and Application, ICIC 2016
AU - Khalaf, Mohammed
AU - Hussain, Abir Jaafar
AU - Al-Jumeily, Dhiya
AU - Keight, Robert
AU - Keenan, Russell
AU - Fergus, Paul
AU - Al-Askar, Haya
AU - Shaw, Andy
AU - Idowu, Ibrahim Olatunji
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - 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 preprocessing 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 froma 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.
AB - 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 preprocessing 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 froma 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.
KW - e-Health
KW - Neural network architectures
KW - Real datasets
KW - Receiver operating characteristic curve
KW - Sickle cell disease
KW - The area under curve
UR - https://www.scopus.com/pages/publications/84978910328
U2 - 10.1007/978-3-319-42291-6_78
DO - 10.1007/978-3-319-42291-6_78
M3 - Conference contribution
AN - SCOPUS:84978910328
SN - 9783319422909
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 784
EP - 795
BT - Intelligent Computing Theories and Application - 12th International Conference, ICIC 2016, Proceedings
A2 - Premaratne, Prashan
A2 - Huang, De-Shuang
A2 - Bevilacqua, Vitoantonio
PB - Springer Verlag
Y2 - 2 August 2016 through 5 August 2016
ER -