Abstract
The pulmonary tuberculosis (TB) is diagnosed conventionally from the test results obtained from different medical examinations. The paper proposes a novel methodology using the classification technique called Identification tree (IDT) to diagnose TB computationally. The model reduces the number of parameters required for the diagnosis substantially. It also offers a list of rules for the speedy and easy diagnosis. The effectiveness of the method has been validated by comparing with existing techniques using standard detection measures.
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Dongardive, J., Xavier, A., Jain, K., Abraham, S. (2011). Classification and Rule-Based Approach to Diagnose Pulmonary Tuberculosis. In: Abraham, A., Lloret Mauri, J., Buford, J.F., Suzuki, J., Thampi, S.M. (eds) Advances in Computing and Communications. ACC 2011. Communications in Computer and Information Science, vol 190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22709-7_34
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DOI: https://doi.org/10.1007/978-3-642-22709-7_34
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