Abstract
Medical diagnosis support is often based on the case based reasoning (CBR) scheme. In accordance with this scheme, the record of a new patient is compared with similar records of previous patients with confirmed diagnosis. Such scheme has been implemented among others in the Hepar system, which comprises a hepathological database and a variety of procedures that aim at data analysis and the support of diagnosis. The diagnosis support rules of this system are based on the visualizing data transformations combined with the nearest neighbors technique. The applied transformations of data sets allow not only for data visualization but also for modifications of the distance or similarity measures used in the nearest neighbors technique. In this way, similarity measures can be induced from data sets.
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Bobrowski, L., Topczewska, M. (2003). Tuning of Diagnosis Support Rules through Visualizing Data Transformations. In: Perner, P., Brause, R., Holzhütter, HG. (eds) Medical Data Analysis. ISMDA 2003. Lecture Notes in Computer Science, vol 2868. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39619-2_3
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DOI: https://doi.org/10.1007/978-3-540-39619-2_3
Publisher Name: Springer, Berlin, Heidelberg
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