Medical Prognosis Generation in Blood Total Test Results

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In this paper, we present two approaches to generate prognosis from general blood test results. The first approach is a knowledge-based approach using ripple-down rules (RDR). The knowledge-based approach with RDR converts knowledge of pathologists into a knowl- edge base with the minimum intervention of knowledge engineers. The second approach is a machine-learning(ML)-based approach using decision tree, random forest and deep neural network (DNN). The ML-based approach learns patterns of attributes from various cases of general blood test. Our experimental results show that there are indeed some important patterns of the attributes in general blood test results, and they are adequately encoded by the both approaches.
School of Engineering and ICT University of Tasmania
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The 29th anniversary of the Australasian Joint Conference on Artificial Intelligence

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CS-Conference Papers(학술회의논문)
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