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Application of an artificial neural network in the prediction of leprosy in anti-leprosy vaccination trial
Artificial Neural Network model (ANN) is a powerful tool to facilitate, classify, analyze and predict an outcome for complex data. This network model has vast application in various fields such as engineering, manufacturing and medicine. Now-a-days ANN is broadly used in the medical field for early diagnosis of diseases. This study attempts to make use of Neural Network in Epidemiological field, especially in anti-leprosy vaccination trail. Multilayer feed forward perceptron network is used for diagnosis of leprosy disease. This study facilitates to compare the performance of neural networks based on its accuracy on both training and testing data set. For constructing the network, the whole data set was divided into inputs and outputs. The demographic, physical and clinical symptoms were considered as inputs and the outputs are identified as cases with ‘1’ and non-cases with ‘0’. The original data were divided into training and testing sample. The training sample was used to develop a model to diagnose the disease. ANN approaches do not provide better results in the prediction of Leprosy.
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