Classification of Control Measures for Asthma using Artificial Neural Networks

N.H.H.M. Hanif, W.H. Lan, H.B. Daud, and J. Ahmad (Malaysia)


Neural Networks, supervised learning, classification


This project is to classify control measures of asthma using artificial neural networks. Asthma is a common disease throughout the whole world. Symptoms of asthma can range from mild to severe. The aim of this project is to classify different severity of asthma and the suitable control measures to overcome it. Neural network architectures were developed for the different severity of asthma. For this preliminary research, three different networks were developed, which are feed forward backpropagation network, Elman backpropagation network and radial basis function network. The most suitable control measures were obtained by training the constructed neural network architectures. The accuracy of the trained architectures was tested by inputting new sets of data to a created Graphical User Interface (GUI). Supervised learning was utilized for this purpose. Based on the works conducted, the radial basis function network achieves accuracy of 90% in classifying the control measures of asthma, which proves that a well trained neural network has a significant capability in classification tasks. To conduct the specified works, MATLAB software were extensively used.

Important Links:

Go Back