An ANN Based Pattern Classification Algorithm for Diagnosis of Swine Flu
Saroj Kr. Biswas
Journal of Intelligent Computing, Vol. 5, No. 1, 2014.
DOI: 
Link: http://www.dline.info/jic/fulltext/v5n1/2.pdf
Abstract:
Embedding Machine Learning technology into Medical Diagnosis Systems adds a new potential to such systems. This paper presents an algorithm using multilayer perceptron based Artificial Neural Network (ANN) for detecting swine flu accurately and efficiently. In this approach once an ANN model is trained using appropriate data patterns of different patients some having swine flu, the model becomes intelligent and is ready for detecting correctly whether the case is a case of swine flu or not. Firstly feature selection is performed, which is driven by the goal of confirming a target class by a feature s discriminating power. A k-NN based algorithm is used to reduce the size of data to be used for training the ANN model with an objective of making the training more efficient and accurate. Then the structure of a pattern is represented by these important features along with a class label. Once the ANN model is trained using patterns of training set related to Swine flu, its performance is evaluated on test pattern set of Swine flu. The results achieved with the proposed approach demonstrate the ability of the algorithm to provide high level of accuracy for the Swine Flu classification problem. When the assessment (classification) ability of the ANN model is compared with that of CBR approaches, the superiority of the neural network approach is established.