Abstract
Orphan diseases post a particular problem to medical expert and data analysts, because of the lack of data resources and sometimes missing effective treatment. In order to shorten the diagnosing time for rare diseases, we have gathered qualitative and quantitative data through clinical observations, interviews and questionnaires of patients who suffer from rare diseases. From the perspective of data analysis, a pattern recognition system based on an ensemble of classifiers was trained to support the diagnosis of rare diseases. Our study shows that the combination of multiple classifiers has better performance in disease prediction than any individual classifier. Furthermore, a testing method was used to better understand the importance and influences of particular symptoms for certain diseases, and to determine how reliable or sensitive the recognition of diseases through small adjustments in the given answers of a patient.
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Kortum, X., Grigull, L., Muecke, U., Lechner, W., Klawonn, F. (2016). Diagnosis Support for Orphan Diseases: A Case Study Using a Classifier Fusion Method. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2016. IDEAL 2016. Lecture Notes in Computer Science(), vol 9937. Springer, Cham. https://doi.org/10.1007/978-3-319-46257-8_41
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DOI: https://doi.org/10.1007/978-3-319-46257-8_41
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