Abstract
This work concern attempts to model imprecise symptoms in the medical diagnosis support tools. Patient’s self-check is very important, particularly in chronic diseases. In hypertension or diabetes patients record measurements. Still, these measurements are made in different circumstances, thus they are imprecise. A physician takes into account rather a trend in a series of measurements to diagnose a patient. Till now, knowledge engineers’ approach is different since they often use a single value as input information of a decision support system. In this work, a series of measurements is modeled as a fuzzy number. The main purpose of the presented approach is to check whether it is possible to replace a single measurement with a series of measurements in the diagnosis support system and to examine the impact of this change on the diagnosis process. Preliminary results show that use of the fuzzy number in determining the diagnosis may increase its certainty and can be profitable when used in real medical problems.
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Acknowledgements
This research is financed from the statutory funds (BKM-510/Rau-3/2017 & BK-232/Rau-3/2017) of the Institute of Electronics of the Silesian University of Technology, Gliwice, Poland.
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Porebski, S., Straszecka, E. (2018). Using Fuzzy Numbers for Modeling Series of Medical Measurements in a Diagnosis Support Based on the Dempster-Shafer Theory. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10842. Springer, Cham. https://doi.org/10.1007/978-3-319-91262-2_20
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